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def adapt_policy(self, exploration_policy, exploration_episodes):
"""Produce a policy adapted for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_trajectories by interacting with an environment.
The caller may not use this object after passing it into this
method.
exploration_episodes (EpisodeBatch): Episodes with which to adapt.
These are generated by exploration_policy while exploring the
environment.
Returns:
Policy: A policy adapted to the task represented by the
exploration_episodes.
"""
|
Produce a policy adapted for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_trajectories by interacting with an environment.
The caller may not use this object after passing it into this
method.
exploration_episodes (EpisodeBatch): Episodes with which to adapt.
These are generated by exploration_policy while exploring the
environment.
Returns:
Policy: A policy adapted to the task represented by the
exploration_episodes.
|
adapt_policy
|
python
|
rlworkgroup/garage
|
src/garage/np/algos/meta_rl_algorithm.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/algos/meta_rl_algorithm.py
|
MIT
|
def optimize_policy(self, paths):
"""Optimize the policy using the samples.
Args:
paths (list[dict]): A list of collected paths.
"""
|
Optimize the policy using the samples.
Args:
paths (list[dict]): A list of collected paths.
|
optimize_policy
|
python
|
rlworkgroup/garage
|
src/garage/np/algos/nop.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/algos/nop.py
|
MIT
|
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Trainer is passed to give algorithm
the access to trainer.step_epochs(), which provides services
such as snapshotting and sampler control.
"""
|
Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Trainer is passed to give algorithm
the access to trainer.step_epochs(), which provides services
such as snapshotting and sampler control.
|
train
|
python
|
rlworkgroup/garage
|
src/garage/np/algos/nop.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/algos/nop.py
|
MIT
|
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Trainer is passed to give algorithm
the access to trainer.step_epochs(), which provides services
such as snapshotting and sampler control.
"""
|
Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Trainer is passed to give algorithm
the access to trainer.step_epochs(), which provides services
such as snapshotting and sampler control.
|
train
|
python
|
rlworkgroup/garage
|
src/garage/np/algos/rl_algorithm.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/algos/rl_algorithm.py
|
MIT
|
def fit(self, paths):
"""Fit regressor based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
"""
|
Fit regressor based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
|
fit
|
python
|
rlworkgroup/garage
|
src/garage/np/baselines/baseline.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/baselines/baseline.py
|
MIT
|
def predict(self, paths):
"""Predict value based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
"""
|
Predict value based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
|
predict
|
python
|
rlworkgroup/garage
|
src/garage/np/baselines/baseline.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/baselines/baseline.py
|
MIT
|
def _features(self, path):
"""Extract features from path.
Args:
path (list[dict]): Sample paths.
Returns:
numpy.ndarray: Extracted features.
"""
obs = np.clip(path['observations'], self.lower_bound, self.upper_bound)
length = len(path['observations'])
al = np.arange(length).reshape(-1, 1) / 100.0
return np.concatenate(
[obs, obs**2, al, al**2, al**3,
np.ones((length, 1))], axis=1)
|
Extract features from path.
Args:
path (list[dict]): Sample paths.
Returns:
numpy.ndarray: Extracted features.
|
_features
|
python
|
rlworkgroup/garage
|
src/garage/np/baselines/linear_feature_baseline.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/baselines/linear_feature_baseline.py
|
MIT
|
def fit(self, paths):
"""Fit regressor based on paths.
Args:
paths (list[dict]): Sample paths.
"""
featmat = np.concatenate([self._features(path) for path in paths])
returns = np.concatenate([path['returns'] for path in paths])
reg_coeff = self._reg_coeff
for _ in range(5):
self._coeffs = np.linalg.lstsq(
featmat.T.dot(featmat) +
reg_coeff * np.identity(featmat.shape[1]),
featmat.T.dot(returns),
rcond=-1)[0]
if not np.any(np.isnan(self._coeffs)):
break
reg_coeff *= 10
|
Fit regressor based on paths.
Args:
paths (list[dict]): Sample paths.
|
fit
|
python
|
rlworkgroup/garage
|
src/garage/np/baselines/linear_feature_baseline.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/baselines/linear_feature_baseline.py
|
MIT
|
def predict(self, paths):
"""Predict value based on paths.
Args:
paths (list[dict]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
"""
if self._coeffs is None:
return np.zeros(len(paths['observations']))
return self._features(paths).dot(self._coeffs)
|
Predict value based on paths.
Args:
paths (list[dict]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
|
predict
|
python
|
rlworkgroup/garage
|
src/garage/np/baselines/linear_feature_baseline.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/baselines/linear_feature_baseline.py
|
MIT
|
def _features(self, path):
"""Extract features from path.
Args:
path (list[dict]): Sample paths.
Returns:
numpy.ndarray: Extracted features.
"""
features = [
np.clip(path[feature_name], -10, 10)
for feature_name in self._feature_names
]
n = len(path['observations'])
return np.concatenate(sum([[f, f**2]
for f in features], []) + [np.ones((n, 1))],
axis=1)
|
Extract features from path.
Args:
path (list[dict]): Sample paths.
Returns:
numpy.ndarray: Extracted features.
|
_features
|
python
|
rlworkgroup/garage
|
src/garage/np/baselines/linear_multi_feature_baseline.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/baselines/linear_multi_feature_baseline.py
|
MIT
|
def reset(self, do_resets=None):
"""Reset the encoder.
This is effective only to recurrent encoder. do_resets is effective
only to vectoried encoder.
For a vectorized encoder, do_resets is an array of boolean indicating
which internal states to be reset. The length of do_resets should be
equal to the length of inputs.
Args:
do_resets (numpy.ndarray): Bool array indicating which states
to be reset.
"""
|
Reset the encoder.
This is effective only to recurrent encoder. do_resets is effective
only to vectoried encoder.
For a vectorized encoder, do_resets is an array of boolean indicating
which internal states to be reset. The length of do_resets should be
equal to the length of inputs.
Args:
do_resets (numpy.ndarray): Bool array indicating which states
to be reset.
|
reset
|
python
|
rlworkgroup/garage
|
src/garage/np/embeddings/encoder.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/embeddings/encoder.py
|
MIT
|
def get_action(self, observation):
"""Get action from this policy for the input observation.
Args:
observation(numpy.ndarray): Observation from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
"""
action, agent_info = self.policy.get_action(observation)
action = np.clip(
action + np.random.normal(size=action.shape) * self._sigma(),
self._action_space.low, self._action_space.high)
self._total_env_steps += 1
return action, agent_info
|
Get action from this policy for the input observation.
Args:
observation(numpy.ndarray): Observation from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
|
get_action
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_gaussian_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_gaussian_noise.py
|
MIT
|
def get_actions(self, observations):
"""Get actions from this policy for the input observation.
Args:
observations(list): Observations from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
"""
actions, agent_infos = self.policy.get_actions(observations)
for itr, _ in enumerate(actions):
actions[itr] = np.clip(
actions[itr] +
np.random.normal(size=actions[itr].shape) * self._sigma(),
self._action_space.low, self._action_space.high)
self._total_env_steps += 1
return actions, agent_infos
|
Get actions from this policy for the input observation.
Args:
observations(list): Observations from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
|
get_actions
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_gaussian_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_gaussian_noise.py
|
MIT
|
def _sigma(self):
"""Get the current sigma.
Returns:
double: Sigma.
"""
if self._total_env_steps >= self._decay_period:
return self._min_sigma
return self._max_sigma - self._decrement * self._total_env_steps
|
Get the current sigma.
Returns:
double: Sigma.
|
_sigma
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_gaussian_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_gaussian_noise.py
|
MIT
|
def update(self, episode_batch):
"""Update the exploration policy using a batch of trajectories.
Args:
episode_batch (EpisodeBatch): A batch of trajectories which
were sampled with this policy active.
"""
self._total_env_steps = (self._last_total_env_steps +
np.sum(episode_batch.lengths))
self._last_total_env_steps = self._total_env_steps
tabular.record('AddGaussianNoise/Sigma', self._sigma())
|
Update the exploration policy using a batch of trajectories.
Args:
episode_batch (EpisodeBatch): A batch of trajectories which
were sampled with this policy active.
|
update
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_gaussian_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_gaussian_noise.py
|
MIT
|
def get_param_values(self):
"""Get parameter values.
Returns:
list or dict: Values of each parameter.
"""
return {
'total_env_steps': self._total_env_steps,
'inner_params': self.policy.get_param_values()
}
|
Get parameter values.
Returns:
list or dict: Values of each parameter.
|
get_param_values
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_gaussian_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_gaussian_noise.py
|
MIT
|
def set_param_values(self, params):
"""Set param values.
Args:
params (np.ndarray): A numpy array of parameter values.
"""
self._total_env_steps = params['total_env_steps']
self.policy.set_param_values(params['inner_params'])
self._last_total_env_steps = self._total_env_steps
|
Set param values.
Args:
params (np.ndarray): A numpy array of parameter values.
|
set_param_values
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_gaussian_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_gaussian_noise.py
|
MIT
|
def _simulate(self):
"""Advance the OU process.
Returns:
np.ndarray: Updated OU process state.
"""
x = self._state
dx = self._theta * (self._mu - x) * self._dt + self._sigma * np.sqrt(
self._dt) * np.random.normal(size=len(x))
self._state = x + dx
return self._state
|
Advance the OU process.
Returns:
np.ndarray: Updated OU process state.
|
_simulate
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_ornstein_uhlenbeck_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_ornstein_uhlenbeck_noise.py
|
MIT
|
def get_action(self, observation):
"""Return an action with noise.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
np.ndarray: An action with noise.
dict: Arbitrary policy state information (agent_info).
"""
action, agent_infos = self.policy.get_action(observation)
ou_state = self._simulate()
return np.clip(action + ou_state, self._action_space.low,
self._action_space.high), agent_infos
|
Return an action with noise.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
np.ndarray: An action with noise.
dict: Arbitrary policy state information (agent_info).
|
get_action
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_ornstein_uhlenbeck_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_ornstein_uhlenbeck_noise.py
|
MIT
|
def get_actions(self, observations):
"""Return actions with noise.
Args:
observations (np.ndarray): Observation from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
"""
actions, agent_infos = self.policy.get_actions(observations)
ou_state = self._simulate()
return np.clip(actions + ou_state, self._action_space.low,
self._action_space.high), agent_infos
|
Return actions with noise.
Args:
observations (np.ndarray): Observation from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
|
get_actions
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/add_ornstein_uhlenbeck_noise.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/add_ornstein_uhlenbeck_noise.py
|
MIT
|
def get_action(self, observation):
"""Get action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from the environment.
Returns:
np.ndarray: An action with noise.
dict: Arbitrary policy state information (agent_info).
"""
opt_action, _ = self.policy.get_action(observation)
if np.random.random() < self._epsilon():
opt_action = self._action_space.sample()
self._total_env_steps += 1
return opt_action, dict()
|
Get action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from the environment.
Returns:
np.ndarray: An action with noise.
dict: Arbitrary policy state information (agent_info).
|
get_action
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
MIT
|
def get_actions(self, observations):
"""Get actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observation from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
"""
opt_actions, _ = self.policy.get_actions(observations)
for itr, _ in enumerate(opt_actions):
if np.random.random() < self._epsilon():
opt_actions[itr] = self._action_space.sample()
self._total_env_steps += 1
return opt_actions, dict()
|
Get actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observation from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
|
get_actions
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
MIT
|
def _epsilon(self):
"""Get the current epsilon.
Returns:
double: Epsilon.
"""
if self._total_env_steps >= self._decay_period:
return self._min_epsilon
return self._max_epsilon - self._decrement * self._total_env_steps
|
Get the current epsilon.
Returns:
double: Epsilon.
|
_epsilon
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
MIT
|
def update(self, episode_batch):
"""Update the exploration policy using a batch of trajectories.
Args:
episode_batch (EpisodeBatch): A batch of trajectories which
were sampled with this policy active.
"""
self._total_env_steps = (self._last_total_env_steps +
np.sum(episode_batch.lengths))
self._last_total_env_steps = self._total_env_steps
tabular.record('EpsilonGreedyPolicy/Epsilon', self._epsilon())
|
Update the exploration policy using a batch of trajectories.
Args:
episode_batch (EpisodeBatch): A batch of trajectories which
were sampled with this policy active.
|
update
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
MIT
|
def get_param_values(self):
"""Get parameter values.
Returns:
list or dict: Values of each parameter.
"""
return {
'total_env_steps': self._total_env_steps,
'inner_params': self.policy.get_param_values()
}
|
Get parameter values.
Returns:
list or dict: Values of each parameter.
|
get_param_values
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
MIT
|
def set_param_values(self, params):
"""Set param values.
Args:
params (np.ndarray): A numpy array of parameter values.
"""
self._total_env_steps = params['total_env_steps']
self.policy.set_param_values(params['inner_params'])
self._last_total_env_steps = self._total_env_steps
|
Set param values.
Args:
params (np.ndarray): A numpy array of parameter values.
|
set_param_values
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/epsilon_greedy_policy.py
|
MIT
|
def get_action(self, observation):
"""Return an action with noise.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
np.ndarray: An action with noise.
dict: Arbitrary policy state information (agent_info).
"""
|
Return an action with noise.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
np.ndarray: An action with noise.
dict: Arbitrary policy state information (agent_info).
|
get_action
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/exploration_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/exploration_policy.py
|
MIT
|
def get_actions(self, observations):
"""Return actions with noise.
Args:
observations (np.ndarray): Observation from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
"""
|
Return actions with noise.
Args:
observations (np.ndarray): Observation from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
|
get_actions
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/exploration_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/exploration_policy.py
|
MIT
|
def update(self, episode_batch):
"""Update the exploration policy using a batch of trajectories.
Args:
episode_batch (EpisodeBatch): A batch of trajectories which
were sampled with this policy active.
"""
|
Update the exploration policy using a batch of trajectories.
Args:
episode_batch (EpisodeBatch): A batch of trajectories which
were sampled with this policy active.
|
update
|
python
|
rlworkgroup/garage
|
src/garage/np/exploration_policies/exploration_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/exploration_policies/exploration_policy.py
|
MIT
|
def reset(self, do_resets=None):
"""Reset policy.
Args:
do_resets (None or list[bool]): Vectorized policy states to reset.
Raises:
ValueError: If do_resets has length greater than 1.
"""
if do_resets is None:
do_resets = [True]
if len(do_resets) > 1:
raise ValueError('FixedPolicy does not support more than one '
'action at a time.')
self._indices[0] = 0
|
Reset policy.
Args:
do_resets (None or list[bool]): Vectorized policy states to reset.
Raises:
ValueError: If do_resets has length greater than 1.
|
reset
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/fixed_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/fixed_policy.py
|
MIT
|
def get_action(self, observation):
"""Get next action.
Args:
observation (np.ndarray): Ignored.
Raises:
ValueError: If policy is currently vectorized (reset was called
with more than one done value).
Returns:
tuple[np.ndarray, dict[str, np.ndarray]]: The action and agent_info
for this time step.
"""
del observation
action = self._scripted_actions[self._indices[0]]
agent_info = self._agent_infos[self._indices[0]]
self._indices[0] += 1
return action, agent_info
|
Get next action.
Args:
observation (np.ndarray): Ignored.
Raises:
ValueError: If policy is currently vectorized (reset was called
with more than one done value).
Returns:
tuple[np.ndarray, dict[str, np.ndarray]]: The action and agent_info
for this time step.
|
get_action
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/fixed_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/fixed_policy.py
|
MIT
|
def get_actions(self, observations):
"""Get next action.
Args:
observations (np.ndarray): Ignored.
Raises:
ValueError: If observations has length greater than 1.
Returns:
tuple[np.ndarray, dict[str, np.ndarray]]: The action and agent_info
for this time step.
"""
if len(observations) != 1:
raise ValueError('FixedPolicy does not support more than one '
'observation at a time.')
action, agent_info = self.get_action(observations[0])
return np.array(
[action]), {k: np.array([v])
for (k, v) in agent_info.items()}
|
Get next action.
Args:
observations (np.ndarray): Ignored.
Raises:
ValueError: If observations has length greater than 1.
Returns:
tuple[np.ndarray, dict[str, np.ndarray]]: The action and agent_info
for this time step.
|
get_actions
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/fixed_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/fixed_policy.py
|
MIT
|
def get_action(self, observation):
"""Get action sampled from the policy.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Actions and extra agent
infos.
"""
|
Get action sampled from the policy.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Actions and extra agent
infos.
|
get_action
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/policy.py
|
MIT
|
def get_actions(self, observations):
"""Get actions given observations.
Args:
observations (torch.Tensor): Observations from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Actions and extra agent
infos.
"""
|
Get actions given observations.
Args:
observations (torch.Tensor): Observations from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Actions and extra agent
infos.
|
get_actions
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/policy.py
|
MIT
|
def reset(self, do_resets=None):
"""Reset the policy.
This is effective only to recurrent policies.
do_resets is an array of boolean indicating
which internal states to be reset. The length of do_resets should be
equal to the length of inputs, i.e. batch size.
Args:
do_resets (numpy.ndarray): Bool array indicating which states
to be reset.
"""
|
Reset the policy.
This is effective only to recurrent policies.
do_resets is an array of boolean indicating
which internal states to be reset. The length of do_resets should be
equal to the length of inputs, i.e. batch size.
Args:
do_resets (numpy.ndarray): Bool array indicating which states
to be reset.
|
reset
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/policy.py
|
MIT
|
def name(self):
"""Name of policy.
Returns:
str: Name of policy
"""
|
Name of policy.
Returns:
str: Name of policy
|
name
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/policy.py
|
MIT
|
def env_spec(self):
"""Policy environment specification.
Returns:
garage.EnvSpec: Environment specification.
"""
|
Policy environment specification.
Returns:
garage.EnvSpec: Environment specification.
|
env_spec
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/policy.py
|
MIT
|
def set_param_values(self, params):
"""Set param values.
Args:
params (np.ndarray): A numpy array of parameter values.
"""
|
Set param values.
Args:
params (np.ndarray): A numpy array of parameter values.
|
set_param_values
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/scripted_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/scripted_policy.py
|
MIT
|
def get_action(self, observation):
"""Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict[dict]: Agent infos indexed by observation.
"""
if self._agent_env_infos:
a_info = self._agent_env_infos[observation]
else:
a_info = dict()
return self._scripted_actions[observation], a_info
|
Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict[dict]: Agent infos indexed by observation.
|
get_action
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/scripted_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/scripted_policy.py
|
MIT
|
def get_actions(self, observations):
"""Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict[dict]: Agent info indexed by observation.
"""
if self._agent_env_infos:
a_info = self._agent_env_infos[observations[0]]
else:
a_info = dict()
return [self._scripted_actions[obs] for obs in observations], a_info
|
Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict[dict]: Agent info indexed by observation.
|
get_actions
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/scripted_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/scripted_policy.py
|
MIT
|
def get_actions(self, observations):
"""Get actions from this policy for the input observation.
Args:
observations(list): Observations from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
"""
return [self._env_spec.action_space.sample()
for obs in observations], dict()
|
Get actions from this policy for the input observation.
Args:
observations(list): Observations from the environment.
Returns:
np.ndarray: Actions with noise.
List[dict]: Arbitrary policy state information (agent_info).
|
get_actions
|
python
|
rlworkgroup/garage
|
src/garage/np/policies/uniform_random_policy.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/np/policies/uniform_random_policy.py
|
MIT
|
def init_plot(self, env, policy):
"""Initialize the plotter.
Args:
env (GymEnv): Environment to visualize.
policy (Policy): Policy to roll out in the
visualization.
"""
if not Plotter.enable:
return
if not (self._process and self._queue):
self._init_worker()
# Needed in order to draw glfw window on the main thread
if 'Darwin' in platform.platform():
rollout(env, policy, max_episode_length=np.inf, animated=True)
self._queue.put(Message(op=Op.UPDATE, args=(env, policy), kwargs=None))
|
Initialize the plotter.
Args:
env (GymEnv): Environment to visualize.
policy (Policy): Policy to roll out in the
visualization.
|
init_plot
|
python
|
rlworkgroup/garage
|
src/garage/plotter/plotter.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/plotter/plotter.py
|
MIT
|
def update_plot(self, policy, max_length=np.inf):
"""Update the plotter.
Args:
policy (garage.np.policies.Policy): New policy to roll out in the
visualization.
max_length (int): Maximum number of steps to roll out.
"""
if not Plotter.enable:
return
self._queue.put(
Message(op=Op.DEMO,
args=(policy.get_param_values(), max_length),
kwargs=None))
|
Update the plotter.
Args:
policy (garage.np.policies.Policy): New policy to roll out in the
visualization.
max_length (int): Maximum number of steps to roll out.
|
update_plot
|
python
|
rlworkgroup/garage
|
src/garage/plotter/plotter.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/plotter/plotter.py
|
MIT
|
def _sample_her_goals(self, path, transition_idx):
"""Samples HER goals from the given path.
Goals are randomly sampled starting from the index after
transition_idx in the given path.
Args:
path (dict[str, np.ndarray]): A dict containing the transition
keys, where each key contains an ndarray of shape
:math:`(T, S^*)`.
transition_idx (int): index of the current transition. Only
transitions after the current transitions will be randomly
sampled for HER goals.
Returns:
np.ndarray: A numpy array of HER goals with shape
(replay_k, goal_dim).
"""
goal_indexes = np.random.randint(transition_idx + 1,
len(path['observations']),
size=self._replay_k)
return [
goal['achieved_goal']
for goal in np.asarray(path['observations'])[goal_indexes]
]
|
Samples HER goals from the given path.
Goals are randomly sampled starting from the index after
transition_idx in the given path.
Args:
path (dict[str, np.ndarray]): A dict containing the transition
keys, where each key contains an ndarray of shape
:math:`(T, S^*)`.
transition_idx (int): index of the current transition. Only
transitions after the current transitions will be randomly
sampled for HER goals.
Returns:
np.ndarray: A numpy array of HER goals with shape
(replay_k, goal_dim).
|
_sample_her_goals
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/her_replay_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/her_replay_buffer.py
|
MIT
|
def add_path(self, path):
"""Adds a path to the replay buffer.
For each transition in the given path except the last one,
replay_k HER transitions will added to the buffer in addition
to the one in the path. The last transition is added without
sampling additional HER goals.
Args:
path(dict[str, np.ndarray]): Each key in the dict must map
to a np.ndarray of shape :math:`(T, S^*)`.
"""
obs_space = self._env_spec.observation_space
if not isinstance(path['observations'][0], dict):
# unflatten dicts if they've been flattened
path['observations'] = obs_space.unflatten_n(path['observations'])
path['next_observations'] = (obs_space.unflatten_n(
path['next_observations']))
# create HER transitions and add them to the buffer
for idx in range(path['actions'].shape[0] - 1):
transition = {key: sample[idx] for key, sample in path.items()}
her_goals = self._sample_her_goals(path, idx)
# create replay_k transitions using the HER goals
for goal in her_goals:
t_new = copy.deepcopy(transition)
a_g = t_new['next_observations']['achieved_goal']
t_new['rewards'] = np.array(self._reward_fn(a_g, goal, None))
t_new['observations']['desired_goal'] = goal
t_new['next_observations']['desired_goal'] = copy.deepcopy(
goal)
t_new['terminals'] = np.array(False)
# flatten the observation dicts now that we're done with them
self._flatten_dicts(t_new)
for key in t_new.keys():
t_new[key] = t_new[key].reshape(1, -1)
# Since we're using a PathBuffer, add each new transition
# as its own path.
super().add_path(t_new)
self._flatten_dicts(path)
super().add_path(path)
|
Adds a path to the replay buffer.
For each transition in the given path except the last one,
replay_k HER transitions will added to the buffer in addition
to the one in the path. The last transition is added without
sampling additional HER goals.
Args:
path(dict[str, np.ndarray]): Each key in the dict must map
to a np.ndarray of shape :math:`(T, S^*)`.
|
add_path
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/her_replay_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/her_replay_buffer.py
|
MIT
|
def add_episode_batch(self, episodes):
"""Add a EpisodeBatch to the buffer.
Args:
episodes (EpisodeBatch): Episodes to add.
"""
if self._env_spec is None:
self._env_spec = episodes.env_spec
env_spec = episodes.env_spec
obs_space = env_spec.observation_space
for eps in episodes.split():
terminals = np.array([
step_type == StepType.TERMINAL for step_type in eps.step_types
],
dtype=bool)
path = {
'observations': obs_space.flatten_n(eps.observations),
'next_observations':
obs_space.flatten_n(eps.next_observations),
'actions': env_spec.action_space.flatten_n(eps.actions),
'rewards': eps.rewards.reshape(-1, 1),
'terminals': terminals.reshape(-1, 1),
}
self.add_path(path)
|
Add a EpisodeBatch to the buffer.
Args:
episodes (EpisodeBatch): Episodes to add.
|
add_episode_batch
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def add_path(self, path):
"""Add a path to the buffer.
Args:
path (dict): A dict of array of shape (path_len, flat_dim).
Raises:
ValueError: If a key is missing from path or path has wrong shape.
"""
for key, buf_arr in self._buffer.items():
path_array = path.get(key, None)
if path_array is None:
raise ValueError('Key {} missing from path.'.format(key))
if (len(path_array.shape) != 2
or path_array.shape[1] != buf_arr.shape[1]):
raise ValueError('Array {} has wrong shape.'.format(key))
path_len = self._get_path_length(path)
first_seg, second_seg = self._next_path_segments(path_len)
# Remove paths which will overlap with this one.
while (self._path_segments and self._segments_overlap(
first_seg, self._path_segments[0][0])):
self._path_segments.popleft()
while (self._path_segments and self._segments_overlap(
second_seg, self._path_segments[0][0])):
self._path_segments.popleft()
self._path_segments.append((first_seg, second_seg))
for key, array in path.items():
buf_arr = self._get_or_allocate_key(key, array)
# numpy doesn't special case range indexing, so it's very slow.
# Slice manually instead, which is faster than any other method.
buf_arr[first_seg.start:first_seg.stop] = array[:len(first_seg)]
buf_arr[second_seg.start:second_seg.stop] = array[len(first_seg):]
if second_seg.stop != 0:
self._first_idx_of_next_path = second_seg.stop
else:
self._first_idx_of_next_path = first_seg.stop
self._transitions_stored = min(self._capacity,
self._transitions_stored + path_len)
|
Add a path to the buffer.
Args:
path (dict): A dict of array of shape (path_len, flat_dim).
Raises:
ValueError: If a key is missing from path or path has wrong shape.
|
add_path
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def sample_path(self):
"""Sample a single path from the buffer.
Returns:
path: A dict of arrays of shape (path_len, flat_dim).
"""
path_idx = np.random.randint(len(self._path_segments))
first_seg, second_seg = self._path_segments[path_idx]
first_seg_indices = np.arange(first_seg.start, first_seg.stop)
second_seg_indices = np.arange(second_seg.start, second_seg.stop)
indices = np.concatenate([first_seg_indices, second_seg_indices])
path = {key: buf_arr[indices] for key, buf_arr in self._buffer.items()}
return path
|
Sample a single path from the buffer.
Returns:
path: A dict of arrays of shape (path_len, flat_dim).
|
sample_path
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def sample_transitions(self, batch_size):
"""Sample a batch of transitions from the buffer.
Args:
batch_size (int): Number of transitions to sample.
Returns:
dict: A dict of arrays of shape (batch_size, flat_dim).
"""
idx = np.random.randint(self._transitions_stored, size=batch_size)
return {key: buf_arr[idx] for key, buf_arr in self._buffer.items()}
|
Sample a batch of transitions from the buffer.
Args:
batch_size (int): Number of transitions to sample.
Returns:
dict: A dict of arrays of shape (batch_size, flat_dim).
|
sample_transitions
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def sample_timesteps(self, batch_size):
"""Sample a batch of timesteps from the buffer.
Args:
batch_size (int): Number of timesteps to sample.
Returns:
TimeStepBatch: The batch of timesteps.
"""
samples = self.sample_transitions(batch_size)
step_types = np.array([
StepType.TERMINAL if terminal else StepType.MID
for terminal in samples['terminals'].reshape(-1)
],
dtype=StepType)
return TimeStepBatch(env_spec=self._env_spec,
episode_infos={},
observations=samples['observations'],
actions=samples['actions'],
rewards=samples['rewards'].flatten(),
next_observations=samples['next_observations'],
step_types=step_types,
env_infos={},
agent_infos={})
|
Sample a batch of timesteps from the buffer.
Args:
batch_size (int): Number of timesteps to sample.
Returns:
TimeStepBatch: The batch of timesteps.
|
sample_timesteps
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def _next_path_segments(self, n_indices):
"""Compute where the next path should be stored.
Args:
n_indices (int): Path length.
Returns:
tuple: Lists of indices where path should be stored.
Raises:
ValueError: If path length is greater than the size of buffer.
"""
if n_indices > self._capacity:
raise ValueError('Path is too long to store in buffer.')
start = self._first_idx_of_next_path
end = start + n_indices
if end > self._capacity:
second_end = end - self._capacity
return (range(start, self._capacity), range(0, second_end))
else:
return (range(start, end), range(0, 0))
|
Compute where the next path should be stored.
Args:
n_indices (int): Path length.
Returns:
tuple: Lists of indices where path should be stored.
Raises:
ValueError: If path length is greater than the size of buffer.
|
_next_path_segments
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def _get_or_allocate_key(self, key, array):
"""Get or allocate key in the buffer.
Args:
key (str): Key in buffer.
array (numpy.ndarray): Array corresponding to key.
Returns:
numpy.ndarray: A NumPy array corresponding to key in the buffer.
"""
buf_arr = self._buffer.get(key, None)
if buf_arr is None:
buf_arr = np.zeros((self._capacity, array.shape[1]), array.dtype)
self._buffer[key] = buf_arr
return buf_arr
|
Get or allocate key in the buffer.
Args:
key (str): Key in buffer.
array (numpy.ndarray): Array corresponding to key.
Returns:
numpy.ndarray: A NumPy array corresponding to key in the buffer.
|
_get_or_allocate_key
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def _get_path_length(path):
"""Get path length.
Args:
path (dict): Path.
Returns:
length: Path length.
Raises:
ValueError: If path is empty or has inconsistent lengths.
"""
length_key = None
length = None
for key, value in path.items():
if length is None:
length = len(value)
length_key = key
elif len(value) != length:
raise ValueError('path has inconsistent lengths between '
'{!r} and {!r}.'.format(length_key, key))
if not length:
raise ValueError('Nothing in path')
return length
|
Get path length.
Args:
path (dict): Path.
Returns:
length: Path length.
Raises:
ValueError: If path is empty or has inconsistent lengths.
|
_get_path_length
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def _segments_overlap(seg_a, seg_b):
"""Compute if two segments overlap.
Args:
seg_a (range): List of indices of the first segment.
seg_b (range): List of indices of the second segment.
Returns:
bool: True iff the input ranges overlap at at least one index.
"""
# Empty segments never overlap.
if not seg_a or not seg_b:
return False
first = seg_a
second = seg_b
if seg_b.start < seg_a.start:
first, second = seg_b, seg_a
assert first.start <= second.start
return first.stop > second.start
|
Compute if two segments overlap.
Args:
seg_a (range): List of indices of the first segment.
seg_b (range): List of indices of the second segment.
Returns:
bool: True iff the input ranges overlap at at least one index.
|
_segments_overlap
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/path_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/path_buffer.py
|
MIT
|
def store_episode(self):
"""Add an episode to the buffer."""
episode_buffer = self._convert_episode_to_batch_major()
episode_batch_size = len(episode_buffer['observation'])
idx = self._get_storage_idx(episode_batch_size)
for key in self._buffer:
self._buffer[key][idx] = episode_buffer[key]
self._n_transitions_stored = min(
self._size_in_transitions, self._n_transitions_stored +
self._time_horizon * episode_batch_size)
|
Add an episode to the buffer.
|
store_episode
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/replay_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/replay_buffer.py
|
MIT
|
def add_transitions(self, **kwargs):
"""Add multiple transitions into the replay buffer.
A transition contains one or multiple entries, e.g.
observation, action, reward, terminal and next_observation.
The same entry of all the transitions are stacked, e.g.
{'observation': [obs1, obs2, obs3]} where obs1 is one
numpy.ndarray observation from the environment.
Args:
kwargs (dict(str, [numpy.ndarray])): Dictionary that holds
the transitions.
"""
if not self._initialized_buffer:
self._initialize_buffer(**kwargs)
for key, value in kwargs.items():
self._episode_buffer[key].append(value)
if len(self._episode_buffer['observation']) == self._time_horizon:
self.store_episode()
for key in self._episode_buffer:
self._episode_buffer[key].clear()
|
Add multiple transitions into the replay buffer.
A transition contains one or multiple entries, e.g.
observation, action, reward, terminal and next_observation.
The same entry of all the transitions are stacked, e.g.
{'observation': [obs1, obs2, obs3]} where obs1 is one
numpy.ndarray observation from the environment.
Args:
kwargs (dict(str, [numpy.ndarray])): Dictionary that holds
the transitions.
|
add_transitions
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/replay_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/replay_buffer.py
|
MIT
|
def _get_storage_idx(self, size_increment=1):
"""Get the storage index for the episode to add into the buffer.
Args:
size_increment(int): The number of storage indeces that new
transitions will be placed in.
Returns:
numpy.ndarray: The indeces to store size_incremente transitions at.
"""
if self._current_size + size_increment <= self._size:
idx = np.arange(self._current_size,
self._current_size + size_increment)
elif self._current_size < self._size:
overflow = size_increment - (self._size - self._current_size)
idx_a = np.arange(self._current_size, self._size)
idx_b = np.arange(0, overflow)
idx = np.concatenate([idx_a, idx_b])
self._current_ptr = overflow
else:
if self._current_ptr + size_increment <= self._size:
idx = np.arange(self._current_ptr,
self._current_ptr + size_increment)
self._current_ptr += size_increment
else:
overflow = size_increment - (self._size - self._current_size)
idx_a = np.arange(self._current_ptr, self._size)
idx_b = np.arange(0, overflow)
idx = np.concatenate([idx_a, idx_b])
self._current_ptr = overflow
# Update replay size
self._current_size = min(self._size,
self._current_size + size_increment)
if size_increment == 1:
idx = idx[0]
return idx
|
Get the storage index for the episode to add into the buffer.
Args:
size_increment(int): The number of storage indeces that new
transitions will be placed in.
Returns:
numpy.ndarray: The indeces to store size_incremente transitions at.
|
_get_storage_idx
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/replay_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/replay_buffer.py
|
MIT
|
def _convert_episode_to_batch_major(self):
"""Convert the shape of episode_buffer.
episode_buffer: {time_horizon, algo.episode_batch_size, flat_dim}.
buffer: {size, time_horizon, flat_dim}.
Returns:
dict: Transitions that have been formated to fit properly in this
replay buffer.
"""
transitions = {}
for key in self._episode_buffer:
val = np.array(self._episode_buffer[key])
transitions[key] = val.swapaxes(0, 1)
return transitions
|
Convert the shape of episode_buffer.
episode_buffer: {time_horizon, algo.episode_batch_size, flat_dim}.
buffer: {size, time_horizon, flat_dim}.
Returns:
dict: Transitions that have been formated to fit properly in this
replay buffer.
|
_convert_episode_to_batch_major
|
python
|
rlworkgroup/garage
|
src/garage/replay_buffer/replay_buffer.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/replay_buffer/replay_buffer.py
|
MIT
|
def update_agent(self, agent_update):
"""Update an agent, assuming it implements :class:`~Policy`.
Args:
agent_update (np.ndarray or dict or Policy): If a tuple, dict, or
np.ndarray, these should be parameters to agent, which should
have been generated by calling `Policy.get_param_values`.
Alternatively, a policy itself. Note that other implementations
of `Worker` may take different types for this parameter.
"""
if isinstance(agent_update, (dict, tuple, np.ndarray)):
self.agent.set_param_values(agent_update)
elif agent_update is not None:
self.agent = agent_update
|
Update an agent, assuming it implements :class:`~Policy`.
Args:
agent_update (np.ndarray or dict or Policy): If a tuple, dict, or
np.ndarray, these should be parameters to agent, which should
have been generated by calling `Policy.get_param_values`.
Alternatively, a policy itself. Note that other implementations
of `Worker` may take different types for this parameter.
|
update_agent
|
python
|
rlworkgroup/garage
|
src/garage/sampler/default_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/default_worker.py
|
MIT
|
def step_episode(self):
"""Take a single time-step in the current episode.
Returns:
bool: True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
"""
if self._eps_length < self._max_episode_length:
a, agent_info = self.agent.get_action(self._prev_obs)
es = self.env.step(a)
self._observations.append(self._prev_obs)
self._env_steps.append(es)
for k, v in agent_info.items():
self._agent_infos[k].append(v)
self._eps_length += 1
if not es.terminal:
self._prev_obs = es.observation
return False
self._lengths.append(self._eps_length)
self._last_observations.append(self._prev_obs)
return True
|
Take a single time-step in the current episode.
Returns:
bool: True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
|
step_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/default_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/default_worker.py
|
MIT
|
def collect_episode(self):
"""Collect the current episode, clearing the internal buffer.
Returns:
EpisodeBatch: A batch of the episodes completed since the last call
to collect_episode().
"""
observations = self._observations
self._observations = []
last_observations = self._last_observations
self._last_observations = []
actions = []
rewards = []
env_infos = defaultdict(list)
step_types = []
for es in self._env_steps:
actions.append(es.action)
rewards.append(es.reward)
step_types.append(es.step_type)
for k, v in es.env_info.items():
env_infos[k].append(v)
self._env_steps = []
agent_infos = self._agent_infos
self._agent_infos = defaultdict(list)
for k, v in agent_infos.items():
agent_infos[k] = np.asarray(v)
for k, v in env_infos.items():
env_infos[k] = np.asarray(v)
episode_infos = self._episode_infos
self._episode_infos = defaultdict(list)
for k, v in episode_infos.items():
episode_infos[k] = np.asarray(v)
lengths = self._lengths
self._lengths = []
return EpisodeBatch(env_spec=self.env.spec,
episode_infos=episode_infos,
observations=np.asarray(observations),
last_observations=np.asarray(last_observations),
actions=np.asarray(actions),
rewards=np.asarray(rewards),
step_types=np.asarray(step_types, dtype=StepType),
env_infos=dict(env_infos),
agent_infos=dict(agent_infos),
lengths=np.asarray(lengths, dtype='i'))
|
Collect the current episode, clearing the internal buffer.
Returns:
EpisodeBatch: A batch of the episodes completed since the last call
to collect_episode().
|
collect_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/default_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/default_worker.py
|
MIT
|
def rollout(self):
"""Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: The collected episode.
"""
self.start_episode()
while not self.step_episode():
pass
return self.collect_episode()
|
Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: The collected episode.
|
rollout
|
python
|
rlworkgroup/garage
|
src/garage/sampler/default_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/default_worker.py
|
MIT
|
def __call__(self, old_env=None):
"""Update an environment.
Args:
old_env (Environment or None): Previous environment. Should not be
used after being passed in, and should not be closed.
Returns:
Environment: The new, updated environment.
"""
if old_env:
old_env.close()
return self._env_constructor()
|
Update an environment.
Args:
old_env (Environment or None): Previous environment. Should not be
used after being passed in, and should not be closed.
Returns:
Environment: The new, updated environment.
|
__call__
|
python
|
rlworkgroup/garage
|
src/garage/sampler/env_update.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/env_update.py
|
MIT
|
def _make_env(self):
"""Construct the environment, wrapping if necessary.
Returns:
garage.Env: The (possibly wrapped) environment.
"""
env = self._env_type()
env.set_task(self._task)
if self._wrapper_cons is not None:
env = self._wrapper_cons(env, self._task)
return env
|
Construct the environment, wrapping if necessary.
Returns:
garage.Env: The (possibly wrapped) environment.
|
_make_env
|
python
|
rlworkgroup/garage
|
src/garage/sampler/env_update.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/env_update.py
|
MIT
|
def __call__(self, old_env=None):
"""Update an environment.
Args:
old_env (Environment or None): Previous environment. Should not be
used after being passed in, and should not be closed.
Returns:
Environment: The new, updated environment.
"""
# We need exact type equality, not just a subtype
# pylint: disable=unidiomatic-typecheck
if old_env is None:
return self._make_env()
elif type(getattr(old_env, 'unwrapped', old_env)) != self._env_type:
warnings.warn('SetTaskEnvUpdate is closing an environment. This '
'may indicate a very slow TaskSampler setup.')
old_env.close()
return self._make_env()
else:
old_env.set_task(self._task)
return old_env
|
Update an environment.
Args:
old_env (Environment or None): Previous environment. Should not be
used after being passed in, and should not be closed.
Returns:
Environment: The new, updated environment.
|
__call__
|
python
|
rlworkgroup/garage
|
src/garage/sampler/env_update.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/env_update.py
|
MIT
|
def __getstate__(self):
"""Get the pickle state.
Returns:
dict: The pickled state.
"""
warnings.warn('ExistingEnvUpdate is generally not the most efficient '
'method of transmitting environments to other '
'processes.')
return self.__dict__
|
Get the pickle state.
Returns:
dict: The pickled state.
|
__getstate__
|
python
|
rlworkgroup/garage
|
src/garage/sampler/env_update.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/env_update.py
|
MIT
|
def update_env(self, env_update):
"""Update the environments.
If passed a list (*inside* this list passed to the Sampler itself),
distributes the environments across the "vectorization" dimension.
Args:
env_update(Environment or EnvUpdate or None): The environment to
replace the existing env with. Note that other implementations
of `Worker` may take different types for this parameter.
Raises:
TypeError: If env_update is not one of the documented types.
ValueError: If the wrong number of updates is passed.
"""
if isinstance(env_update, list):
if len(env_update) != self._n_envs:
raise ValueError('If separate environments are passed for '
'each worker, there must be exactly n_envs '
'({}) environments, but received {} '
'environments.'.format(
self._n_envs, len(env_update)))
elif env_update is not None:
env_update = [
copy.deepcopy(env_update) for _ in range(self._n_envs)
]
if env_update:
for env_index, env_up in enumerate(env_update):
self._envs[env_index], up = _apply_env_update(
self._envs[env_index], env_up)
self._needs_env_reset |= up
|
Update the environments.
If passed a list (*inside* this list passed to the Sampler itself),
distributes the environments across the "vectorization" dimension.
Args:
env_update(Environment or EnvUpdate or None): The environment to
replace the existing env with. Note that other implementations
of `Worker` may take different types for this parameter.
Raises:
TypeError: If env_update is not one of the documented types.
ValueError: If the wrong number of updates is passed.
|
update_env
|
python
|
rlworkgroup/garage
|
src/garage/sampler/fragment_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/fragment_worker.py
|
MIT
|
def start_episode(self):
"""Resets all agents if the environment was updated."""
if self._needs_env_reset:
self._needs_env_reset = False
self.agent.reset([True] * len(self._envs))
self._episode_lengths = [0] * len(self._envs)
self._fragments = [InProgressEpisode(env) for env in self._envs]
|
Resets all agents if the environment was updated.
|
start_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/fragment_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/fragment_worker.py
|
MIT
|
def step_episode(self):
"""Take a single time-step in the current episode.
Returns:
bool: True iff at least one of the episodes was completed.
"""
prev_obs = np.asarray([frag.last_obs for frag in self._fragments])
actions, agent_infos = self.agent.get_actions(prev_obs)
completes = [False] * len(self._envs)
for i, action in enumerate(actions):
frag = self._fragments[i]
if self._episode_lengths[i] < self._max_episode_length:
agent_info = {k: v[i] for (k, v) in agent_infos.items()}
frag.step(action, agent_info)
self._episode_lengths[i] += 1
if (self._episode_lengths[i] >= self._max_episode_length
or frag.step_types[-1] == StepType.TERMINAL):
self._episode_lengths[i] = 0
complete_frag = frag.to_batch()
self._complete_fragments.append(complete_frag)
self._fragments[i] = InProgressEpisode(self._envs[i])
completes[i] = True
if any(completes):
self.agent.reset(completes)
return any(completes)
|
Take a single time-step in the current episode.
Returns:
bool: True iff at least one of the episodes was completed.
|
step_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/fragment_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/fragment_worker.py
|
MIT
|
def collect_episode(self):
"""Gather fragments from all in-progress episodes.
Returns:
EpisodeBatch: A batch of the episode fragments.
"""
for i, frag in enumerate(self._fragments):
assert frag.env is self._envs[i]
if len(frag.rewards) > 0:
complete_frag = frag.to_batch()
self._complete_fragments.append(complete_frag)
self._fragments[i] = InProgressEpisode(frag.env, frag.last_obs,
frag.episode_info)
assert len(self._complete_fragments) > 0
result = EpisodeBatch.concatenate(*self._complete_fragments)
self._complete_fragments = []
return result
|
Gather fragments from all in-progress episodes.
Returns:
EpisodeBatch: A batch of the episode fragments.
|
collect_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/fragment_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/fragment_worker.py
|
MIT
|
def rollout(self):
"""Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: The collected episode.
"""
self.start_episode()
for _ in range(self._timesteps_per_call):
self.step_episode()
complete_frag = self.collect_episode()
return complete_frag
|
Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: The collected episode.
|
rollout
|
python
|
rlworkgroup/garage
|
src/garage/sampler/fragment_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/fragment_worker.py
|
MIT
|
def _update_workers(self, agent_update, env_update):
"""Apply updates to the workers.
Args:
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
"""
agent_updates = self._factory.prepare_worker_messages(agent_update)
env_updates = self._factory.prepare_worker_messages(
env_update, preprocess=copy.deepcopy)
for worker, agent_up, env_up in zip(self._workers, agent_updates,
env_updates):
worker.update_agent(agent_up)
worker.update_env(env_up)
|
Apply updates to the workers.
Args:
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
|
_update_workers
|
python
|
rlworkgroup/garage
|
src/garage/sampler/local_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/local_sampler.py
|
MIT
|
def obtain_samples(self, itr, num_samples, agent_update, env_update=None):
"""Collect at least a given number transitions (timesteps).
Args:
itr(int): The current iteration number. Using this argument is
deprecated.
num_samples (int): Minimum number of transitions / timesteps to
sample.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: The batch of collected episodes.
"""
self._update_workers(agent_update, env_update)
batches = []
completed_samples = 0
while True:
for worker in self._workers:
batch = worker.rollout()
completed_samples += len(batch.actions)
batches.append(batch)
if completed_samples >= num_samples:
samples = EpisodeBatch.concatenate(*batches)
self.total_env_steps += sum(samples.lengths)
return samples
|
Collect at least a given number transitions (timesteps).
Args:
itr(int): The current iteration number. Using this argument is
deprecated.
num_samples (int): Minimum number of transitions / timesteps to
sample.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: The batch of collected episodes.
|
obtain_samples
|
python
|
rlworkgroup/garage
|
src/garage/sampler/local_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/local_sampler.py
|
MIT
|
def obtain_exact_episodes(self,
n_eps_per_worker,
agent_update,
env_update=None):
"""Sample an exact number of episodes per worker.
Args:
n_eps_per_worker (int): Exact number of episodes to gather for
each worker.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before samplin episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes. Always in worker
order. In other words, first all episodes from worker 0,
then all episodes from worker 1, etc.
"""
self._update_workers(agent_update, env_update)
batches = []
for worker in self._workers:
for _ in range(n_eps_per_worker):
batch = worker.rollout()
batches.append(batch)
samples = EpisodeBatch.concatenate(*batches)
self.total_env_steps += sum(samples.lengths)
return samples
|
Sample an exact number of episodes per worker.
Args:
n_eps_per_worker (int): Exact number of episodes to gather for
each worker.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before samplin episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes. Always in worker
order. In other words, first all episodes from worker 0,
then all episodes from worker 1, etc.
|
obtain_exact_episodes
|
python
|
rlworkgroup/garage
|
src/garage/sampler/local_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/local_sampler.py
|
MIT
|
def __getstate__(self):
"""Get the pickle state.
Returns:
dict: The pickled state.
"""
state = self.__dict__.copy()
# Workers aren't picklable (but WorkerFactory is).
state['_workers'] = None
return state
|
Get the pickle state.
Returns:
dict: The pickled state.
|
__getstate__
|
python
|
rlworkgroup/garage
|
src/garage/sampler/local_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/local_sampler.py
|
MIT
|
def __setstate__(self, state):
"""Unpickle the state.
Args:
state (dict): Unpickled state.
"""
self.__dict__.update(state)
self._workers = [
self._factory(i) for i in range(self._factory.n_workers)
]
for worker, agent, env in zip(self._workers, self._agents, self._envs):
worker.update_agent(agent)
worker.update_env(env)
|
Unpickle the state.
Args:
state (dict): Unpickled state.
|
__setstate__
|
python
|
rlworkgroup/garage
|
src/garage/sampler/local_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/local_sampler.py
|
MIT
|
def _push_updates(self, updated_workers, agent_updates, env_updates):
"""Apply updates to the workers and (re)start them.
Args:
updated_workers (set[int]): Set of workers that don't need to be
updated. Successfully updated workers will be added to this
set.
agent_updates (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_updates (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
"""
for worker_number, q in enumerate(self._to_worker):
if worker_number in updated_workers:
try:
q.put_nowait(('continue', ()))
except queue.Full:
pass
else:
try:
q.put_nowait(('start', (agent_updates[worker_number],
env_updates[worker_number],
self._agent_version)))
updated_workers.add(worker_number)
except queue.Full:
pass
|
Apply updates to the workers and (re)start them.
Args:
updated_workers (set[int]): Set of workers that don't need to be
updated. Successfully updated workers will be added to this
set.
agent_updates (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_updates (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
|
_push_updates
|
python
|
rlworkgroup/garage
|
src/garage/sampler/multiprocessing_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/multiprocessing_sampler.py
|
MIT
|
def obtain_samples(self, itr, num_samples, agent_update, env_update=None):
"""Collect at least a given number transitions (timesteps).
Args:
itr(int): The current iteration number. Using this argument is
deprecated.
num_samples (int): Minimum number of transitions / timesteps to
sample.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: The batch of collected episodes.
Raises:
AssertionError: On internal errors.
"""
del itr
batches = []
completed_samples = 0
self._agent_version += 1
updated_workers = set()
agent_ups = self._factory.prepare_worker_messages(
agent_update, cloudpickle.dumps)
env_ups = self._factory.prepare_worker_messages(
env_update, cloudpickle.dumps)
with click.progressbar(length=num_samples, label='Sampling') as pbar:
while completed_samples < num_samples:
self._push_updates(updated_workers, agent_ups, env_ups)
for _ in range(self._factory.n_workers):
try:
tag, contents = self._to_sampler.get_nowait()
if tag == 'episode':
batch, version, worker_n = contents
del worker_n
if version == self._agent_version:
batches.append(batch)
num_returned_samples = batch.lengths.sum()
completed_samples += num_returned_samples
pbar.update(num_returned_samples)
else:
# Receiving episodes from previous iterations
# is normal. Potentially, we could gather them
# here, if an off-policy method wants them.
pass
else:
raise AssertionError(
'Unknown tag {} with contents {}'.format(
tag, contents))
except queue.Empty:
pass
for q in self._to_worker:
try:
q.put_nowait(('stop', ()))
except queue.Full:
pass
samples = EpisodeBatch.concatenate(*batches)
self.total_env_steps += sum(samples.lengths)
return samples
|
Collect at least a given number transitions (timesteps).
Args:
itr(int): The current iteration number. Using this argument is
deprecated.
num_samples (int): Minimum number of transitions / timesteps to
sample.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: The batch of collected episodes.
Raises:
AssertionError: On internal errors.
|
obtain_samples
|
python
|
rlworkgroup/garage
|
src/garage/sampler/multiprocessing_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/multiprocessing_sampler.py
|
MIT
|
def obtain_exact_episodes(self,
n_eps_per_worker,
agent_update,
env_update=None):
"""Sample an exact number of episodes per worker.
Args:
n_eps_per_worker (int): Exact number of episodes to gather for
each worker.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes. Always in worker
order. In other words, first all episodes from worker 0,
then all episodes from worker 1, etc.
Raises:
AssertionError: On internal errors.
"""
self._agent_version += 1
updated_workers = set()
agent_ups = self._factory.prepare_worker_messages(
agent_update, cloudpickle.dumps)
env_ups = self._factory.prepare_worker_messages(
env_update, cloudpickle.dumps)
episodes = defaultdict(list)
with click.progressbar(length=self._factory.n_workers,
label='Sampling') as pbar:
while any(
len(episodes[i]) < n_eps_per_worker
for i in range(self._factory.n_workers)):
self._push_updates(updated_workers, agent_ups, env_ups)
tag, contents = self._to_sampler.get()
if tag == 'episode':
batch, version, worker_n = contents
if version == self._agent_version:
if len(episodes[worker_n]) < n_eps_per_worker:
episodes[worker_n].append(batch)
if len(episodes[worker_n]) == n_eps_per_worker:
pbar.update(1)
try:
self._to_worker[worker_n].put_nowait(
('stop', ()))
except queue.Full:
pass
else:
raise AssertionError(
'Unknown tag {} with contents {}'.format(
tag, contents))
for q in self._to_worker:
try:
q.put_nowait(('stop', ()))
except queue.Full:
pass
ordered_episodes = list(
itertools.chain(
*[episodes[i] for i in range(self._factory.n_workers)]))
samples = EpisodeBatch.concatenate(*ordered_episodes)
self.total_env_steps += sum(samples.lengths)
return samples
|
Sample an exact number of episodes per worker.
Args:
n_eps_per_worker (int): Exact number of episodes to gather for
each worker.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes. Always in worker
order. In other words, first all episodes from worker 0,
then all episodes from worker 1, etc.
Raises:
AssertionError: On internal errors.
|
obtain_exact_episodes
|
python
|
rlworkgroup/garage
|
src/garage/sampler/multiprocessing_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/multiprocessing_sampler.py
|
MIT
|
def __getstate__(self):
"""Get the pickle state.
Returns:
dict: The pickled state.
"""
return dict(
factory=self._factory,
agents=[cloudpickle.loads(agent) for agent in self._agents],
envs=[cloudpickle.loads(env) for env in self._envs])
|
Get the pickle state.
Returns:
dict: The pickled state.
|
__getstate__
|
python
|
rlworkgroup/garage
|
src/garage/sampler/multiprocessing_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/multiprocessing_sampler.py
|
MIT
|
def _update_workers(self, agent_update, env_update):
"""Update all of the workers.
Args:
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling_episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
list[ray._raylet.ObjectID]: Remote values of worker ids.
"""
updating_workers = []
param_ids = self._worker_factory.prepare_worker_messages(
agent_update, ray.put)
env_ids = self._worker_factory.prepare_worker_messages(
env_update, ray.put)
for worker_id in range(self._worker_factory.n_workers):
worker = self._all_workers[worker_id]
updating_workers.append(
worker.update.remote(param_ids[worker_id], env_ids[worker_id]))
return updating_workers
|
Update all of the workers.
Args:
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling_episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
list[ray._raylet.ObjectID]: Remote values of worker ids.
|
_update_workers
|
python
|
rlworkgroup/garage
|
src/garage/sampler/ray_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/ray_sampler.py
|
MIT
|
def obtain_samples(self, itr, num_samples, agent_update, env_update=None):
"""Sample the policy for new episodes.
Args:
itr (int): Iteration number.
num_samples (int): Number of steps the the sampler should collect.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes.
"""
active_workers = []
completed_samples = 0
batches = []
# update the policy params of each worker before sampling
# for the current iteration
idle_worker_ids = []
updating_workers = self._update_workers(agent_update, env_update)
with click.progressbar(length=num_samples, label='Sampling') as pbar:
while completed_samples < num_samples:
# if there are workers still being updated, check
# which ones are still updating and take the workers that
# are done updating, and start collecting episodes on those
# workers.
if updating_workers:
updated, updating_workers = ray.wait(updating_workers,
num_returns=1,
timeout=0.1)
upd = [ray.get(up) for up in updated]
idle_worker_ids.extend(upd)
# if there are idle workers, use them to collect episodes and
# mark the newly busy workers as active
while idle_worker_ids:
idle_worker_id = idle_worker_ids.pop()
worker = self._all_workers[idle_worker_id]
active_workers.append(worker.rollout.remote())
# check which workers are done/not done collecting a sample
# if any are done, send them to process the collected
# episode if they are not, keep checking if they are done
ready, not_ready = ray.wait(active_workers,
num_returns=1,
timeout=0.001)
active_workers = not_ready
for result in ready:
ready_worker_id, episode_batch = ray.get(result)
idle_worker_ids.append(ready_worker_id)
num_returned_samples = episode_batch.lengths.sum()
completed_samples += num_returned_samples
batches.append(episode_batch)
pbar.update(num_returned_samples)
samples = EpisodeBatch.concatenate(*batches)
self.total_env_steps += sum(samples.lengths)
return samples
|
Sample the policy for new episodes.
Args:
itr (int): Iteration number.
num_samples (int): Number of steps the the sampler should collect.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes.
|
obtain_samples
|
python
|
rlworkgroup/garage
|
src/garage/sampler/ray_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/ray_sampler.py
|
MIT
|
def obtain_exact_episodes(self,
n_eps_per_worker,
agent_update,
env_update=None):
"""Sample an exact number of episodes per worker.
Args:
n_eps_per_worker (int): Exact number of episodes to gather for
each worker.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes. Always in worker
order. In other words, first all episodes from worker 0, then
all episodes from worker 1, etc.
"""
active_workers = []
episodes = defaultdict(list)
# update the policy params of each worker before sampling
# for the current iteration
idle_worker_ids = []
updating_workers = self._update_workers(agent_update, env_update)
with click.progressbar(length=self._worker_factory.n_workers,
label='Sampling') as pbar:
while any(
len(episodes[i]) < n_eps_per_worker
for i in range(self._worker_factory.n_workers)):
# if there are workers still being updated, check
# which ones are still updating and take the workers that
# are done updating, and start collecting episodes on
# those workers.
if updating_workers:
updated, updating_workers = ray.wait(updating_workers,
num_returns=1,
timeout=0.1)
upd = [ray.get(up) for up in updated]
idle_worker_ids.extend(upd)
# if there are idle workers, use them to collect episodes
# mark the newly busy workers as active
while idle_worker_ids:
idle_worker_id = idle_worker_ids.pop()
worker = self._all_workers[idle_worker_id]
active_workers.append(worker.rollout.remote())
# check which workers are done/not done collecting a sample
# if any are done, send them to process the collected episode
# if they are not, keep checking if they are done
ready, not_ready = ray.wait(active_workers,
num_returns=1,
timeout=0.001)
active_workers = not_ready
for result in ready:
ready_worker_id, episode_batch = ray.get(result)
episodes[ready_worker_id].append(episode_batch)
if len(episodes[ready_worker_id]) < n_eps_per_worker:
idle_worker_ids.append(ready_worker_id)
pbar.update(1)
ordered_episodes = list(
itertools.chain(
*[episodes[i] for i in range(self._worker_factory.n_workers)]))
samples = EpisodeBatch.concatenate(*ordered_episodes)
self.total_env_steps += sum(samples.lengths)
return samples
|
Sample an exact number of episodes per worker.
Args:
n_eps_per_worker (int): Exact number of episodes to gather for
each worker.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: Batch of gathered episodes. Always in worker
order. In other words, first all episodes from worker 0, then
all episodes from worker 1, etc.
|
obtain_exact_episodes
|
python
|
rlworkgroup/garage
|
src/garage/sampler/ray_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/ray_sampler.py
|
MIT
|
def update(self, agent_update, env_update):
"""Update the agent and environment.
Args:
agent_update (object): Agent update.
env_update (object): Environment update.
Returns:
int: The worker id.
"""
self.inner_worker.update_agent(agent_update)
self.inner_worker.update_env(env_update)
return self.worker_id
|
Update the agent and environment.
Args:
agent_update (object): Agent update.
env_update (object): Environment update.
Returns:
int: The worker id.
|
update
|
python
|
rlworkgroup/garage
|
src/garage/sampler/ray_sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/ray_sampler.py
|
MIT
|
def __init__(self, algo, env):
"""Construct a Sampler from an Algorithm.
Args:
algo (RLAlgorithm): The RL Algorithm controlling this
sampler.
env (Environment): The environment being sampled from.
Calling this method is deprecated.
"""
self.algo = algo
self.env = env
|
Construct a Sampler from an Algorithm.
Args:
algo (RLAlgorithm): The RL Algorithm controlling this
sampler.
env (Environment): The environment being sampled from.
Calling this method is deprecated.
|
__init__
|
python
|
rlworkgroup/garage
|
src/garage/sampler/sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/sampler.py
|
MIT
|
def start_worker(self):
"""Initialize the sampler.
i.e. launching parallel workers if necessary.
This method is deprecated, please launch workers in construct instead.
"""
|
Initialize the sampler.
i.e. launching parallel workers if necessary.
This method is deprecated, please launch workers in construct instead.
|
start_worker
|
python
|
rlworkgroup/garage
|
src/garage/sampler/sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/sampler.py
|
MIT
|
def obtain_samples(self, itr, num_samples, agent_update, env_update=None):
"""Collect at least a given number transitions :class:`TimeStep`s.
Args:
itr (int): The current iteration number. Using this argument is
deprecated.
num_samples (int): Minimum number of :class:`TimeStep`s to sample.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: The batch of collected episodes.
"""
|
Collect at least a given number transitions :class:`TimeStep`s.
Args:
itr (int): The current iteration number. Using this argument is
deprecated.
num_samples (int): Minimum number of :class:`TimeStep`s to sample.
agent_update (object): Value which will be passed into the
`agent_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
env_update (object): Value which will be passed into the
`env_update_fn` before sampling episodes. If a list is passed
in, it must have length exactly `factory.n_workers`, and will
be spread across the workers.
Returns:
EpisodeBatch: The batch of collected episodes.
|
obtain_samples
|
python
|
rlworkgroup/garage
|
src/garage/sampler/sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/sampler.py
|
MIT
|
def shutdown_worker(self):
"""Terminate workers if necessary.
Because Python object destruction can be somewhat unpredictable, this
method isn't deprecated.
"""
|
Terminate workers if necessary.
Because Python object destruction can be somewhat unpredictable, this
method isn't deprecated.
|
shutdown_worker
|
python
|
rlworkgroup/garage
|
src/garage/sampler/sampler.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/sampler.py
|
MIT
|
def rollout(env,
agent,
*,
max_episode_length=np.inf,
animated=False,
speedup=1,
deterministic=False):
"""Sample a single episode of the agent in the environment.
Args:
agent (Policy): Agent used to select actions.
env (Environment): Environment to perform actions in.
max_episode_length (int): If the episode reaches this many timesteps,
it is truncated.
animated (bool): If true, render the environment after each step.
speedup (float): Factor by which to decrease the wait time between
rendered steps. Only relevant, if animated == true.
deterministic (bool): If true, use the mean action returned by the
stochastic policy instead of sampling from the returned action
distribution.
Returns:
dict[str, np.ndarray or dict]: Dictionary, with keys:
* observations(np.array): Flattened array of observations.
There should be one more of these than actions. Note that
observations[i] (for i < len(observations) - 1) was used by the
agent to choose actions[i]. Should have shape (T + 1, S^*) (the
unflattened state space of the current environment).
* actions(np.array): Non-flattened array of actions. Should have
shape (T, S^*) (the unflattened action space of the current
environment).
* rewards(np.array): Array of rewards of shape (T,) (1D array of
length timesteps).
* agent_infos(Dict[str, np.array]): Dictionary of stacked,
non-flattened `agent_info` arrays.
* env_infos(Dict[str, np.array]): Dictionary of stacked,
non-flattened `env_info` arrays.
* episode_infos(Dict[str, np.array]): Dictionary of stacked,
non-flattened `episode_info` arrays.
* dones(np.array): Array of termination signals.
"""
del speedup
env_steps = []
agent_infos = []
observations = []
episode_infos = []
last_obs, episode_info = env.reset()
agent.reset()
episode_length = 0
if animated:
env.visualize()
while episode_length < (max_episode_length or np.inf):
a, agent_info = agent.get_action(last_obs)
if deterministic and 'mean' in agent_info:
a = agent_info['mean']
es = env.step(a)
env_steps.append(es)
observations.append(last_obs)
agent_infos.append(agent_info)
episode_infos.append(episode_info)
episode_length += 1
if es.last:
break
last_obs = es.observation
return dict(
observations=np.array(observations),
actions=np.array([es.action for es in env_steps]),
rewards=np.array([es.reward for es in env_steps]),
agent_infos=stack_tensor_dict_list(agent_infos),
env_infos=stack_tensor_dict_list([es.env_info for es in env_steps]),
episode_infos=stack_tensor_dict_list(episode_infos),
dones=np.array([es.terminal for es in env_steps]),
)
|
Sample a single episode of the agent in the environment.
Args:
agent (Policy): Agent used to select actions.
env (Environment): Environment to perform actions in.
max_episode_length (int): If the episode reaches this many timesteps,
it is truncated.
animated (bool): If true, render the environment after each step.
speedup (float): Factor by which to decrease the wait time between
rendered steps. Only relevant, if animated == true.
deterministic (bool): If true, use the mean action returned by the
stochastic policy instead of sampling from the returned action
distribution.
Returns:
dict[str, np.ndarray or dict]: Dictionary, with keys:
* observations(np.array): Flattened array of observations.
There should be one more of these than actions. Note that
observations[i] (for i < len(observations) - 1) was used by the
agent to choose actions[i]. Should have shape (T + 1, S^*) (the
unflattened state space of the current environment).
* actions(np.array): Non-flattened array of actions. Should have
shape (T, S^*) (the unflattened action space of the current
environment).
* rewards(np.array): Array of rewards of shape (T,) (1D array of
length timesteps).
* agent_infos(Dict[str, np.array]): Dictionary of stacked,
non-flattened `agent_info` arrays.
* env_infos(Dict[str, np.array]): Dictionary of stacked,
non-flattened `env_info` arrays.
* episode_infos(Dict[str, np.array]): Dictionary of stacked,
non-flattened `episode_info` arrays.
* dones(np.array): Array of termination signals.
|
rollout
|
python
|
rlworkgroup/garage
|
src/garage/sampler/utils.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/utils.py
|
MIT
|
def truncate_paths(paths, max_samples):
"""Truncate the paths so that the total number of samples is max_samples.
This is done by removing extra paths at the end of
the list, and make the last path shorter if necessary
Args:
paths (list[dict[str, np.ndarray]]): Samples, items with keys:
* observations (np.ndarray): Enviroment observations
* actions (np.ndarray): Agent actions
* rewards (np.ndarray): Environment rewards
* env_infos (dict): Environment state information
* agent_infos (dict): Agent state information
max_samples(int) : Maximum number of samples allowed.
Returns:
list[dict[str, np.ndarray]]: A list of paths, truncated so that the
number of samples adds up to max-samples
Raises:
ValueError: If key a other than 'observations', 'actions', 'rewards',
'env_infos' and 'agent_infos' is found.
"""
# chop samples collected by extra paths
# make a copy
valid_keys = {
'observations', 'actions', 'rewards', 'env_infos', 'agent_infos'
}
paths = list(paths)
total_n_samples = sum(len(path['rewards']) for path in paths)
while paths and total_n_samples - len(paths[-1]['rewards']) >= max_samples:
total_n_samples -= len(paths.pop(-1)['rewards'])
if paths:
last_path = paths.pop(-1)
truncated_last_path = dict()
truncated_len = len(
last_path['rewards']) - (total_n_samples - max_samples)
for k, v in last_path.items():
if k in ['observations', 'actions', 'rewards']:
truncated_last_path[k] = v[:truncated_len]
elif k in ['env_infos', 'agent_infos']:
truncated_last_path[k] = truncate_tensor_dict(v, truncated_len)
else:
raise ValueError(
'Unexpected key {} found in path. Valid keys: {}'.format(
k, valid_keys))
paths.append(truncated_last_path)
return paths
|
Truncate the paths so that the total number of samples is max_samples.
This is done by removing extra paths at the end of
the list, and make the last path shorter if necessary
Args:
paths (list[dict[str, np.ndarray]]): Samples, items with keys:
* observations (np.ndarray): Enviroment observations
* actions (np.ndarray): Agent actions
* rewards (np.ndarray): Environment rewards
* env_infos (dict): Environment state information
* agent_infos (dict): Agent state information
max_samples(int) : Maximum number of samples allowed.
Returns:
list[dict[str, np.ndarray]]: A list of paths, truncated so that the
number of samples adds up to max-samples
Raises:
ValueError: If key a other than 'observations', 'actions', 'rewards',
'env_infos' and 'agent_infos' is found.
|
truncate_paths
|
python
|
rlworkgroup/garage
|
src/garage/sampler/utils.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/utils.py
|
MIT
|
def update_agent(self, agent_update):
"""Update an agent, assuming it implements :class:`~Policy`.
Args:
agent_update (np.ndarray or dict or Policy): If a
tuple, dict, or np.ndarray, these should be parameters to
agent, which should have been generated by calling
`Policy.get_param_values`. Alternatively, a policy itself. Note
that other implementations of `Worker` may take different types
for this parameter.
"""
super().update_agent(agent_update)
self._needs_agent_reset = True
|
Update an agent, assuming it implements :class:`~Policy`.
Args:
agent_update (np.ndarray or dict or Policy): If a
tuple, dict, or np.ndarray, these should be parameters to
agent, which should have been generated by calling
`Policy.get_param_values`. Alternatively, a policy itself. Note
that other implementations of `Worker` may take different types
for this parameter.
|
update_agent
|
python
|
rlworkgroup/garage
|
src/garage/sampler/vec_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/vec_worker.py
|
MIT
|
def update_env(self, env_update):
"""Update the environments.
If passed a list (*inside* this list passed to the Sampler itself),
distributes the environments across the "vectorization" dimension.
Args:
env_update(Environment or EnvUpdate or None): The environment to
replace the existing env with. Note that other implementations
of `Worker` may take different types for this parameter.
Raises:
TypeError: If env_update is not one of the documented types.
ValueError: If the wrong number of updates is passed.
"""
if isinstance(env_update, list):
if len(env_update) != self._n_envs:
raise ValueError('If separate environments are passed for '
'each worker, there must be exactly n_envs '
'({}) environments, but received {} '
'environments.'.format(
self._n_envs, len(env_update)))
elif env_update is not None:
env_update = [
copy.deepcopy(env_update) for _ in range(self._n_envs)
]
if env_update:
for env_index, env_up in enumerate(env_update):
self._envs[env_index], up = _apply_env_update(
self._envs[env_index], env_up)
self._needs_env_reset |= up
|
Update the environments.
If passed a list (*inside* this list passed to the Sampler itself),
distributes the environments across the "vectorization" dimension.
Args:
env_update(Environment or EnvUpdate or None): The environment to
replace the existing env with. Note that other implementations
of `Worker` may take different types for this parameter.
Raises:
TypeError: If env_update is not one of the documented types.
ValueError: If the wrong number of updates is passed.
|
update_env
|
python
|
rlworkgroup/garage
|
src/garage/sampler/vec_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/vec_worker.py
|
MIT
|
def step_episode(self):
"""Take a single time-step in the current episode.
Returns:
bool: True iff at least one of the episodes was completed.
"""
finished = False
actions, agent_info = self.agent.get_actions(self._prev_obs)
completes = [False] * len(self._envs)
for i, action in enumerate(actions):
if self._episode_lengths[i] < self._max_episode_length:
es = self._envs[i].step(action)
self._observations[i].append(self._prev_obs[i])
self._rewards[i].append(es.reward)
self._actions[i].append(es.action)
for k, v in agent_info.items():
self._agent_infos[i][k].append(v[i])
for k, v in es.env_info.items():
self._env_infos[i][k].append(v)
self._episode_lengths[i] += 1
self._step_types[i].append(es.step_type)
self._prev_obs[i] = es.observation
if self._episode_lengths[i] >= self._max_episode_length or es.last:
self._gather_episode(i, es.observation)
completes[i] = True
finished = True
if finished:
self.agent.reset(completes)
return finished
|
Take a single time-step in the current episode.
Returns:
bool: True iff at least one of the episodes was completed.
|
step_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/vec_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/vec_worker.py
|
MIT
|
def collect_episode(self):
"""Collect all completed episodes.
Returns:
EpisodeBatch: A batch of the episodes completed since the last call
to collect_episode().
"""
if len(self._completed_episodes) == 1:
result = self._completed_episodes[0]
else:
result = EpisodeBatch.concatenate(*self._completed_episodes)
self._completed_episodes = []
return result
|
Collect all completed episodes.
Returns:
EpisodeBatch: A batch of the episodes completed since the last call
to collect_episode().
|
collect_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/vec_worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/vec_worker.py
|
MIT
|
def __init__(self, *, seed, max_episode_length, worker_number):
"""Initialize a worker.
Args:
seed (int): The seed to use to intialize random number generators.
max_episode_length (int or float): The maximum length of episodes
which will be sampled. Can be (floating point) infinity.
worker_number (int): The number of the worker this update is
occurring in. This argument is used to set a different seed for
each worker.
Should create fields the following fields:
agent (Policy or None): The worker's initial agent.
env (Environment or None): The worker's environment.
"""
self._seed = seed
self._max_episode_length = max_episode_length
self._worker_number = worker_number
|
Initialize a worker.
Args:
seed (int): The seed to use to intialize random number generators.
max_episode_length (int or float): The maximum length of episodes
which will be sampled. Can be (floating point) infinity.
worker_number (int): The number of the worker this update is
occurring in. This argument is used to set a different seed for
each worker.
Should create fields the following fields:
agent (Policy or None): The worker's initial agent.
env (Environment or None): The worker's environment.
|
__init__
|
python
|
rlworkgroup/garage
|
src/garage/sampler/worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker.py
|
MIT
|
def update_agent(self, agent_update):
"""Update the worker's agent, using agent_update.
Args:
agent_update (object): An agent update. The exact type of this
argument depends on the `Worker` implementation.
"""
|
Update the worker's agent, using agent_update.
Args:
agent_update (object): An agent update. The exact type of this
argument depends on the `Worker` implementation.
|
update_agent
|
python
|
rlworkgroup/garage
|
src/garage/sampler/worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker.py
|
MIT
|
def update_env(self, env_update):
"""Update the worker's env, using env_update.
Args:
env_update (object): An environment update. The exact type of this
argument depends on the `Worker` implementation.
"""
|
Update the worker's env, using env_update.
Args:
env_update (object): An environment update. The exact type of this
argument depends on the `Worker` implementation.
|
update_env
|
python
|
rlworkgroup/garage
|
src/garage/sampler/worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker.py
|
MIT
|
def rollout(self):
"""Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: Batch of sampled episodes. May be truncated if
max_episode_length is set.
"""
|
Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: Batch of sampled episodes. May be truncated if
max_episode_length is set.
|
rollout
|
python
|
rlworkgroup/garage
|
src/garage/sampler/worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker.py
|
MIT
|
def step_episode(self):
"""Take a single time-step in the current episode.
Returns:
True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
"""
|
Take a single time-step in the current episode.
Returns:
True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
|
step_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker.py
|
MIT
|
def collect_episode(self):
"""Collect the current episode, clearing the internal buffer.
Returns:
EpisodeBatch: Batch of sampled episodes. May be truncated if the
episodes haven't completed yet.
"""
|
Collect the current episode, clearing the internal buffer.
Returns:
EpisodeBatch: Batch of sampled episodes. May be truncated if the
episodes haven't completed yet.
|
collect_episode
|
python
|
rlworkgroup/garage
|
src/garage/sampler/worker.py
|
https://github.com/rlworkgroup/garage/blob/master/src/garage/sampler/worker.py
|
MIT
|
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