araffin commited on
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README.md CHANGED
@@ -31,7 +31,9 @@ with hyperparameter optimization and pre-trained agents included.
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  ## Usage (with SB3 RL Zoo)
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- RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo
 
 
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  ```
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  # Download model and save it into the logs/ folder
@@ -55,3 +57,8 @@ OrderedDict([('ent_coef', 0.0),
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  ('policy', 'MlpPolicy'),
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  ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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  ```
 
 
 
 
 
 
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  ## Usage (with SB3 RL Zoo)
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+ 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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  ('policy', 'MlpPolicy'),
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  ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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  ```
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+
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+ # Environment Arguments
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+ ```python
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+ {'goal_velocity': 0}
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+ ```
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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 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 ",
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@@ -91,6 +91,6 @@
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  "normalize_advantage": false,
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  "_last_dones": {
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  }
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  }
 
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  {
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  "policy_class": {
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  ":type:": "<class 'abc.ABCMeta'>",
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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 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 ",
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+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe534a777a0>",
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+ "forward": "<function ActorCriticPolicy.forward at 0x7fe534a779e0>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe534a77a70>",
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+ "_predict": "<function ActorCriticPolicy._predict at 0x7fe534a77b00>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe534a77b90>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe534a77c20>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe534a77cb0>",
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  "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7fe534a4b390>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {
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  ":type:": "<class 'dict'>",
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  "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
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  "optimizer_kwargs": {
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  "alpha": 0.99,
 
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  "observation_space": {
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  "dtype": "float32",
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  "low": "[-1.2 -0.07]",
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  "high": "[0.6 0.07]",
 
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  "action_space": {
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env_kwargs.yml CHANGED
@@ -1 +1 @@
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- {}
 
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+ goal_velocity: 0
results.json CHANGED
@@ -1 +1 @@
1
- {"mean_reward": -110.6, "std_reward": 19.422667170087635, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-20T09:35:49.509692"}
 
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