InvertedDoublePendulumBulletEnv-v0_v3

InvertedDoublePendulumBulletEnv-v0-InvertedDoublePendulumBulletEnv-v0-100k.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:5c8b38206d78bdc2f522548119dbe70811cfbc79ee3274e785f0362a940afbc9
 size 104416

InvertedDoublePendulumBulletEnv-v0-InvertedDoublePendulumBulletEnv-v0-100k/data

@@ -64,7 +64,7 @@
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     "seed": 42,
     "action_noise": null,
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+    "start_time": 1680553184885313565,
     "learning_rate": 0.00096,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -89,7 +89,7 @@
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README.md

@@ -16,7 +16,7 @@ model-index:
       type: InvertedDoublePendulumBulletEnv-v0
     metrics:
     - type: mean_reward
-      value: 893.84 +/- 187.94
+      value: 1129.72 +/- 346.09
       name: mean_reward
       verified: false
 ---

config.json

@@ -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 0x7efc3af89550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7efc3af895e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7efc3af89670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7efc3af89700>", "_build": "<function ActorCriticPolicy._build at 0x7efc3af89790>", "forward": "<function ActorCriticPolicy.forward at 0x7efc3af89820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7efc3af898b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7efc3af89940>", "_predict": "<function ActorCriticPolicy._predict at 0x7efc3af899d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7efc3af89a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7efc3af89af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7efc3af89b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7efc3af88c80>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": 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results.json

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vec_normalize.pkl

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