stable_baselines3/mpdqn/mpdqn.py
def _sample_action(
        self, learning_starts: int, action_noise: Optional[ActionNoise] = None, n_envs: int = 1
    ) -> Tuple[np.ndarray, np.ndarray]:
        """
        Sample an action according to the exploration policy.
        This is either done by sampling the probability distribution of the policy,
        or sampling a random action (from a uniform distribution over the action space)
        or by adding noise to the deterministic output.
        :param action_noise: Action noise that will be used for exploration
            Required for deterministic policy (e.g. TD3). This can also be used
            in addition to the stochastic policy for SAC.
        :param learning_starts: Number of steps before learning for the warm-up phase.
        :return: action to take in the environment
            and scaled action that will be stored in the replay buffer.
            The two differs when the action space is not normalized (bounds are not [-1, 1]).
        """
        return PDQN._sample_action(self, learning_starts, action_noise, n_envs)

        Sample an action according to the exploration policy.
        This is either done by sampling the probability distribution of the policy,
        or sampling a random action (from a uniform distribution over the action space)
        or by adding noise to the deterministic output.
        :param action_noise: Action noise that will be used for exploration
            Required for deterministic policy (e.g. TD3). This can also be used
            in addition to the stochastic policy for SAC.
        :param learning_starts: Number of steps before learning for the warm-up phase.
        :return: action to take in the environment
            and scaled action that will be stored in the replay buffer.
            The two differs when the action space is not normalized (bounds are not [-1, 1]).
        
Sample an action according to the exploration policy.
This is either done by sampling the probability distribution of the policy,
or sampling a random action (from a uniform distribution over the action space)
or by adding noise to the deterministic output.
Sample an action according to the exploration policy . This is either done by sampling the probability distribution of the policy or sampling a random action ( from a uniform distribution over the action space ) or by adding noise to the deterministic output .
def _sample_action(
        self, learning_starts: int, action_noise: Optional[ActionNoise] = None, n_envs: int = 1
    ) -> Tuple[np.ndarray, np.ndarray]:
        return PDQN._sample_action(self, learning_starts, action_noise, n_envs)
def _sample_action ( self , learning_starts : int , action_noise : Optional [ ActionNoise ] = None , n_envs : int = 1 ) -> Tuple [ np . ndarray , np . ndarray ] : return PDQN . _sample_action ( self , learning_starts , action_noise , n_envs )
Sample an action according to the exploration policy.
Sample an action according to the exploration policy .
"""
        Sample an action according to the exploration policy.
        This is either done by sampling the probability distribution of the policy,
        or sampling a random action (from a uniform distribution over the action space)
        or by adding noise to the deterministic output.
        :param action_noise: Action noise that will be used for exploration
            Required for deterministic policy (e.g. TD3). This can also be used
            in addition to the stochastic policy for SAC.
        :param learning_starts: Number of steps before learning for the warm-up phase.
        :return: action to take in the environment
            and scaled action that will be stored in the replay buffer.
            The two differs when the action space is not normalized (bounds are not [-1, 1]).
        """
action to take in the environment
and scaled action that will be stored in the replay buffer.
The two differs when the action space is not normalized (bounds are not [-1, 1]).
action to take in the environment and scaled action that will be stored in the replay buffer . The two differs when the action space is not normalized ( bounds are not [ - 1 1 ] ) .
self
learning_starts
int
Number of steps before learning for the warm-up phase.
Number of steps before learning for the warm - up phase .
action_noise
Optional[ActionNoise]
Action noise that will be used for exploration
Required for deterministic policy . This can also be used
in addition to the stochastic policy for SAC.
Action noise that will be used for exploration Required for deterministic policy . This can also be used in addition to the stochastic policy for SAC .
n_envs
int