
# **PPO** Agent playing **seals/Ant-v0**
This is a trained model of a **PPO** agent playing **seals/Ant-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.

## Usage (with SB3 RL Zoo)

RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env seals/Ant-v0 -orga HumanCompatibleAI -f logs/
python enjoy.py --algo ppo --env seals/Ant-v0  -f logs/
```

If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env seals/Ant-v0 -orga HumanCompatibleAI -f logs/
rl_zoo3 enjoy --algo ppo --env seals/Ant-v0  -f logs/
```

## Training (with the RL Zoo)
```
python train.py --algo ppo --env seals/Ant-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env seals/Ant-v0 -f logs/ -orga HumanCompatibleAI
```

## Hyperparameters
```python
OrderedDict([('batch_size', 16),
             ('clip_range', 0.3),
             ('ent_coef', 3.1441389214159857e-06),
             ('gae_lambda', 0.8),
             ('gamma', 0.995),
             ('learning_rate', 0.00017959211641976886),
             ('max_grad_norm', 0.9),
             ('n_epochs', 10),
             ('n_steps', 2048),
             ('n_timesteps', 1000000.0),
             ('normalize',
              {'gamma': 0.995, 'norm_obs': False, 'norm_reward': True}),
             ('policy', 'MlpPolicy'),
             ('policy_kwargs',
              {'activation_fn': <class 'torch.nn.modules.activation.Tanh'>,
               'features_extractor_class': <class 'imitation.policies.base.NormalizeFeaturesExtractor'>,
               'net_arch': [{'pi': [64, 64], 'vf': [64, 64]}]}),
             ('vf_coef', 0.4351450387648799),
             ('normalize_kwargs',
              {'norm_obs': {'gamma': 0.995,
                            'norm_obs': False,
                            'norm_reward': True},
               'norm_reward': False})])
```

{"library_name": "stable-baselines3", "tags": ["seals/Ant-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "seals/Ant-v0", "type": "seals/Ant-v0"}, "metrics": [{"type": "mean_reward", "value": "3034.50 +/- 1124.70", "name": "mean_reward"}]}]}]}
stable-baselines3 seals/Ant-v0 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #seals/Ant-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# PPO Agent playing seals/Ant-v0
This is a trained model of a PPO agent playing seals/Ant-v0
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.

## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL



If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:


## Training (with the RL Zoo)


## Hyperparameters


# PPO Agent playing seals/Ant-v0
This is a trained model of a PPO agent playing seals/Ant-v0
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL



If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters
TAGS
#stable-baselines3 #seals/Ant-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # PPO Agent playing seals/Ant-v0
This is a trained model of a PPO agent playing seals/Ant-v0
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL



If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters
passage: TAGS
#stable-baselines3 #seals/Ant-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# PPO Agent playing seals/Ant-v0
This is a trained model of a PPO agent playing seals/Ant-v0
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL



If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters