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README.md +1 -1
SAC-Humanoid-v4.zip +2 -2
SAC-Humanoid-v4/actor.optimizer.pth +2 -2
SAC-Humanoid-v4/critic.optimizer.pth +2 -2
SAC-Humanoid-v4/data +68 -53
SAC-Humanoid-v4/ent_coef_optimizer.pth +2 -2
SAC-Humanoid-v4/policy.pth +1 -1
SAC-Humanoid-v4/pytorch_variables.pth +1 -1
config.json +1 -1
replay.mp4 +2 -2
results.json +1 -1

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ model-index:
  type: Humanoid-v4
  metrics:
  - type: mean_reward
- value: 120.94 +/- 6.30
  name: mean_reward
  verified: false
 ---

  type: Humanoid-v4
  metrics:
  - type: mean_reward
+ value: 5409.73 +/- 43.39
  name: mean_reward
  verified: false
 ---

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  "__module__": "stable_baselines3.sac.policies",
  "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}",
  "__doc__": "\n Policy class (with both actor and critic) for SAC.\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\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 :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
+ "__init__": "<function SACPolicy.__init__ at 0x138c47240>",
+ "_build": "<function SACPolicy._build at 0x138c47880>",
+ "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x138c47920>",
+ "reset_noise": "<function SACPolicy.reset_noise at 0x138c479c0>",
+ "make_actor": "<function SACPolicy.make_actor at 0x138c47a60>",
+ "make_critic": "<function SACPolicy.make_critic at 0x138c47b00>",
+ "forward": "<function SACPolicy.forward at 0x138c47ba0>",
+ "_predict": "<function SACPolicy._predict at 0x138c47c40>",
+ "set_training_mode": "<function SACPolicy.set_training_mode at 0x138c47ce0>",
  "__abstractmethods__": "frozenset()",
+ "_abc_impl": "<_abc._abc_data object at 0x138c61180>"
  },
  "verbose": 0,
  "policy_kwargs": {
  "use_sde": false
  },
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+ "_total_timesteps": 1000000,
  "_num_timesteps_at_start": 0,
  "seed": null,
  "action_noise": null,
+ "start_time": 1718033144905816089,
  "learning_rate": 0.0003,
  "tensorboard_log": null,
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+ "learning_starts": 10000,
+ "tau": 0.005,
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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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\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 :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", "__init__": "<function SACPolicy.__init__ at 0x167f5f1a0>", "_build": "<function SACPolicy._build at 0x167f5f7e0>", "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x167f5f880>", 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