ernestum commited on
Commit
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Browse files
README.md CHANGED
@@ -8,16 +8,17 @@ tags:
8
  model-index:
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  - name: PPO
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  results:
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- - metrics:
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- - type: mean_reward
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- value: 500.00 +/- 0.00
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- name: mean_reward
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- task:
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  type: reinforcement-learning
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  name: reinforcement-learning
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  dataset:
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  name: seals/CartPole-v0
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  type: seals/CartPole-v0
 
 
 
 
 
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  ---
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  # **PPO** Agent playing **seals/CartPole-v0**
@@ -35,21 +36,26 @@ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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  SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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  SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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  ```
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  # Download model and save it into the logs/ folder
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  python -m rl_zoo3.load_from_hub --algo ppo --env seals/CartPole-v0 -orga HumanCompatibleAI -f logs/
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- python enjoy.py --algo ppo --env seals/CartPole-v0 -f logs/
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  ```
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  If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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  ```
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  python -m rl_zoo3.load_from_hub --algo ppo --env seals/CartPole-v0 -orga HumanCompatibleAI -f logs/
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- rl_zoo3 enjoy --algo ppo --env seals/CartPole-v0 -f logs/
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  ```
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  ## Training (with the RL Zoo)
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  ```
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- python train.py --algo ppo --env seals/CartPole-v0 -f logs/
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  # Upload the model and generate video (when possible)
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  python -m rl_zoo3.push_to_hub --algo ppo --env seals/CartPole-v0 -f logs/ -orga HumanCompatibleAI
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  ```
@@ -74,3 +80,8 @@ OrderedDict([('batch_size', 256),
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  ('vf_coef', 0.489343896591493),
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  ('normalize', False)])
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  ```
 
 
 
 
 
 
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  model-index:
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  - name: PPO
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  results:
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+ - task:
 
 
 
 
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  type: reinforcement-learning
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  name: reinforcement-learning
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  dataset:
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  name: seals/CartPole-v0
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  type: seals/CartPole-v0
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+ - type: mean_reward
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+ value: 500.00 +/- 0.00
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+ name: mean_reward
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+ verified: false
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  ---
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  # **PPO** Agent playing **seals/CartPole-v0**
 
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  SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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  SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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+ Install the RL Zoo (with SB3 and SB3-Contrib):
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+ ```bash
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+ pip install rl_zoo3
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+ ```
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+
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  ```
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  # Download model and save it into the logs/ folder
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  python -m rl_zoo3.load_from_hub --algo ppo --env seals/CartPole-v0 -orga HumanCompatibleAI -f logs/
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+ python -m rl_zoo3.enjoy --algo ppo --env seals/CartPole-v0 -f logs/
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  ```
49
 
50
  If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
51
  ```
52
  python -m rl_zoo3.load_from_hub --algo ppo --env seals/CartPole-v0 -orga HumanCompatibleAI -f logs/
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+ python -m rl_zoo3.enjoy --algo ppo --env seals/CartPole-v0 -f logs/
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  ```
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  ## Training (with the RL Zoo)
57
  ```
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+ python -m rl_zoo3.train --algo ppo --env seals/CartPole-v0 -f logs/
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  # Upload the model and generate video (when possible)
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  python -m rl_zoo3.push_to_hub --algo ppo --env seals/CartPole-v0 -f logs/ -orga HumanCompatibleAI
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  ```
 
80
  ('vf_coef', 0.489343896591493),
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  ('normalize', False)])
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  ```
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+
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+ # Environment Arguments
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+ ```python
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+ {'render_mode': 'rgb_array'}
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+ ```
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  },
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- "target_kl": null
 
 
 
 
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  }
 
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  "__module__": "stable_baselines3.common.policies",
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+ "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
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+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7fcc418e2ee0>",
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+ "_build": "<function ActorCriticPolicy._build at 0x7fcc41867160>",
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+ "forward": "<function ActorCriticPolicy.forward at 0x7fcc418671f0>",
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+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcc41867280>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcc41867310>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcc418674c0>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcc41867550>",
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  "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc_data object at 0x7fcc418cdc90>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {
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  "activation_fn": "<class 'torch.nn.modules.activation.ReLU'>",
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+ "net_arch": {
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+ "pi": [
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+ 64,
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+ 64
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+ ],
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+ "vf": [
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+ 64,
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+ 64
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+ }
 
 
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  },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "num_timesteps": 102400,
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  "_total_timesteps": 100000,
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+ "seed": 0,
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