araffin commited on
Commit
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Browse files
README.md CHANGED
@@ -23,7 +23,11 @@ model-index:
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  # **PPO** Agent playing **CartPole-v1**
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  This is a trained model of a **PPO** agent playing **CartPole-v1**
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  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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- and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo)
 
 
 
 
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  ## Usage (with SB3 RL Zoo)
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@@ -43,3 +47,19 @@ python train.py --algo ppo --env CartPole-v1 -f logs/
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  # Upload the model and generate video (when possible)
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  python -m utils.push_to_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3
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  ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # **PPO** Agent playing **CartPole-v1**
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  This is a trained model of a **PPO** agent playing **CartPole-v1**
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  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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+
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+ The RL Zoo is a training framework for Stable Baselines3
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+ reinforcement learning agents,
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+ with hyperparameter optimization and pre-trained agents included.
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  ## Usage (with SB3 RL Zoo)
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  # Upload the model and generate video (when possible)
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  python -m utils.push_to_hub --algo ppo --env CartPole-v1 -f logs/ -orga sb3
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  ```
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+
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+ ## Hyperparameters
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+ ```python
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+ OrderedDict([('batch_size', 256),
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+ ('clip_range', 'lin_0.2'),
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+ ('ent_coef', 0.0),
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+ ('gae_lambda', 0.8),
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+ ('gamma', 0.98),
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+ ('learning_rate', 'lin_0.001'),
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+ ('n_envs', 8),
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+ ('n_epochs', 20),
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+ ('n_steps', 32),
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+ ('n_timesteps', 100000.0),
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+ ('policy', 'MlpPolicy'),
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+ ('normalize', False)])
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+ ```
args.yml CHANGED
@@ -1,28 +1,24 @@
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  !!python/object/apply:collections.OrderedDict
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  - - - algo
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  - ppo
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- - - device
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- - auto
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  - - env
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  - CartPole-v1
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  - - env_kwargs
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  - null
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- - sb3
 
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  !!python/object/apply:collections.OrderedDict
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  "gae_lambda": 0.8,
@@ -86,9 +83,13 @@
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  },
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  "normalize_advantage": true,
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- "target_kl": null
 
 
 
 
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  }
 
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  "__module__": "stable_baselines3.common.policies",
6
  "__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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7f66a4faba70>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f66a4fabb00>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f66a4fabb90>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f66a4fabc20>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f66a4fabcb0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f66a4fabd40>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f66a4fabdd0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f66a4fabe60>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f66a4fabef0>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f66a4fabf80>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f66a4fb2050>",
18
  "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f66a4fff660>"
20
  },
21
  "verbose": 1,
22
  "policy_kwargs": {},
23
  "observation_space": {
24
  ":type:": "<class 'gym.spaces.box.Box'>",
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  "dtype": "float32",
 
 
 
27
  "low": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]",
28
  "high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
29
  "bounded_below": "[ True True True True]",
30
  "bounded_above": "[ True True True True]",
31
+ "_np_random": null,
32
+ "_shape": [
33
+ 4
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+ ]
35
  },
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  "action_space": {
37
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