dlantonia commited on
Commit
2526589
1 Parent(s): 4d34069

Upload PPO Pendulum-v1 trained agent

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README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
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  type: Pendulum-v1
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  metrics:
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  - type: mean_reward
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- value: -628.51 +/- 39.39
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  name: mean_reward
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  verified: false
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  ---
 
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  type: Pendulum-v1
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  metrics:
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  - type: mean_reward
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+ value: -421.52 +/- 342.09
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  name: mean_reward
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  verified: false
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  ---
config.json CHANGED
@@ -1 +1 @@
1
- {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x79ae83617520>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79ae836175b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79ae83617640>", 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"action_noise": null, "start_time": 1722880280815447130, "learning_rate": 0.001, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAADf37D5q7mK/ib6mQJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsDhpSMAUOUdJRSlC4="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": true, "sde_sample_freq": 4, "_current_progress_remaining": -0.0035199999999999676, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": 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rollout used in model-based RL or planning.\n Hence, it is only involved in policy and value function training but not action selection.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n Equivalent to classic advantage when set to 1.\n :param gamma: Discount factor\n :param n_envs: Number of parallel environments\n ", "__init__": "<function RolloutBuffer.__init__ at 0x79ae83572050>", "reset": "<function RolloutBuffer.reset at 0x79ae835720e0>", "compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x79ae83572170>", "add": "<function RolloutBuffer.add at 0x79ae83572200>", "get": "<function RolloutBuffer.get at 0x79ae83572290>", "_get_samples": "<function RolloutBuffer._get_samples at 0x79ae83572320>", "__abstractmethods__": 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  }
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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 0x795124afc0d0>",
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+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x795124afc160>",
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+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x795124afc1f0>",
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+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x795124afc280>",
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+ "_build": "<function ActorCriticPolicy._build at 0x795124afc310>",
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+ "forward": "<function ActorCriticPolicy.forward at 0x795124afc3a0>",
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+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x795124afc430>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x795124afc4c0>",
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+ "_predict": "<function ActorCriticPolicy._predict at 0x795124afc550>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x795124afc5e0>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x795124afc670>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x795124afc700>",
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  "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc._abc_data object at 0x795124aa4f40>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {},
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+ "_total_timesteps": 200000,
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  "_num_timesteps_at_start": 0,
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  "seed": null,
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  "action_noise": null,
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  "tensorboard_log": null,
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  },
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  "_last_episode_starts": {
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  },
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+ "use_sde": false,
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+ "sde_sample_freq": -1,
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  "_stats_window_size": 100,
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  "ep_info_buffer": {
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  },
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  "n_envs": 1,
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  "n_steps": 2048,
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  "gae_lambda": 0.95,
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  "ent_coef": 0.0,
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  "vf_coef": 0.5,
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  "max_grad_norm": 0.5,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  "batch_size": 64,
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  "n_epochs": 10,
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  "clip_range": {
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