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Luca77
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a2c-AntBulletEnv-v0

Reinforcement Learning
stable-baselines3
AntBulletEnv-v0
deep-reinforcement-learning
Eval Results
Model card Files Community
Luca77 commited on
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1 Parent(s): f184557

version 2

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Files changed (11) hide show
  1. README.md +1 -1
  2. a2c-AntBulletEnv-v0.zip +2 -2
  3. a2c-AntBulletEnv-v0/_stable_baselines3_version +1 -1
  4. a2c-AntBulletEnv-v0/data +48 -47
  5. a2c-AntBulletEnv-v0/policy.optimizer.pth +1 -1
  6. a2c-AntBulletEnv-v0/policy.pth +2 -2
  7. a2c-AntBulletEnv-v0/system_info.txt +3 -3
  8. config.json +1 -1
  9. replay.mp4 +2 -2
  10. results.json +1 -1
  11. vec_normalize.pkl +2 -2
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: 1473.14 +/- 369.75
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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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a2c-AntBulletEnv-v0/_stable_baselines3_version CHANGED
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a2c-AntBulletEnv-v0/data CHANGED
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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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  },
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  "ep_success_buffer": {
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@@ -102,5 +76,32 @@
102
  "ent_coef": 0.0,
103
  "vf_coef": 0.4,
104
  "max_grad_norm": 0.5,
105
- "normalize_advantage": false
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
106
  }
 
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5
  "__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 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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7f0d34b063b0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0d34b06440>",
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+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0d34b064d0>",
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+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0d34b06560>",
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+ "_build": "<function ActorCriticPolicy._build at 0x7f0d34b065f0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f0d34b06680>",
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+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0d34b06710>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0d34b067a0>",
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+ "_predict": "<function ActorCriticPolicy._predict at 0x7f0d34b06830>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0d34b068c0>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0d34b06950>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0d34b069e0>",
19
  "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7f0d34af3cc0>"
21
  },
22
  "verbose": 1,
23
  "policy_kwargs": {
 
32
  "weight_decay": 0
33
  }
34
  },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
35
  "num_timesteps": 2000000,
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  "_total_timesteps": 2000000,
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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": 1683645729204772775,
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+ "learning_rate": 0.0001,
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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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  "_last_obs": {
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  },
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  "_last_episode_starts": {
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  },
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  "_last_original_obs": {
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  ":type:": "<class 'numpy.ndarray'>",
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+ ":serialized:": "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"
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  },
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  "_episode_num": 0,
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  "use_sde": true,
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  "sde_sample_freq": -1,
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  "_current_progress_remaining": 0.0,
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+ "_stats_window_size": 100,
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  "ep_info_buffer": {
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