armargolis commited on
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1 Parent(s): a3b2e09

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README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
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  type: AntBulletEnv-v0
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  metrics:
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  - type: mean_reward
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- value: 1264.54 +/- 56.63
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  name: mean_reward
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  verified: false
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  ---
 
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  type: AntBulletEnv-v0
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  metrics:
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  - type: mean_reward
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  name: mean_reward
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  verified: false
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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 0x7f4f106bc310>",
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- "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f106bc3a0>",
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- "_build": "<function ActorCriticPolicy._build at 0x7f4f106bc550>",
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- "forward": "<function ActorCriticPolicy.forward at 0x7f4f106bc5e0>",
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- "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4f106bc700>",
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- "_predict": "<function ActorCriticPolicy._predict at 0x7f4f106bc790>",
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- "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4f106bc820>",
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- "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4f106bc8b0>",
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- "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4f106bc940>",
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  "__abstractmethods__": "frozenset()",
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- "_abc_impl": "<_abc_data object at 0x7f4f106b48a0>"
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  },
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  "verbose": 1,
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"
77
  },
78
  "_last_episode_starts": {
79
  ":type:": "<class 'numpy.ndarray'>",
@@ -81,23 +81,23 @@
81
  },
82
  "_last_original_obs": {
83
  ":type:": "<class 'numpy.ndarray'>",
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  },
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  "n_steps": 8,
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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 0x7fbe1c6ccb80>",
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+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbe1c6ccc10>",
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+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbe1c6ccca0>",
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+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbe1c6ccd30>",
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+ "_build": "<function ActorCriticPolicy._build at 0x7fbe1c6ccdc0>",
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+ "forward": "<function ActorCriticPolicy.forward at 0x7fbe1c6cce50>",
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+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbe1c6ccee0>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbe1c6ccf70>",
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+ "_predict": "<function ActorCriticPolicy._predict at 0x7fbe1c6d0040>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbe1c6d00d0>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbe1c6d0160>",
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+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbe1c6d01f0>",
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  "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc._abc_data object at 0x7fbe1c6d1100>"
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  },
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  "verbose": 1,
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  "policy_kwargs": {
 
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  "_np_random": null
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  },
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  "n_envs": 64,
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+ "_total_timesteps": 3000000,
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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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+ "start_time": 1678546492599425479,
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+ "learning_rate": 0.002,
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  "tensorboard_log": null,
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  "lr_schedule": {
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  ":type:": "<class 'function'>",
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  },
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77
  },
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  ":type:": "<class 'numpy.ndarray'>",
 
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  },
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  ":type:": "<class 'numpy.ndarray'>",
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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 0x7f4f106bc310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f106bc3a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f106bc430>", 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