Upload PPO HumanoidStandup-v4 trained agent

HumanoidStandup-v4.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 765837
+oid sha256:2e92503e0468f5b4d244f7adebc3c13e6e0054bf202bb23ef94e877708138d9d
+size 762357

HumanoidStandup-v4/data

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         "__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 0x7c6dbcde7490>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c6dbcde7520>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c6dbcde75b0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c6dbcde7640>",
-        "_build": "<function ActorCriticPolicy._build at 0x7c6dbcde76d0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7c6dbcde7760>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c6dbcde77f0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c6dbcde7880>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7c6dbcde7910>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c6dbcde79a0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c6dbcde7a30>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c6dbcde7ac0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7ee54eda2290>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ee54eda2320>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ee54eda23b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ee54eda2440>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ee54eda24d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ee54eda2560>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ee54eda25f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ee54eda2680>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ee54eda2710>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ee54eda27a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ee54eda2830>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ee54eda28c0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7c6dbcd98800>"
+        "_abc_impl": "<_abc._abc_data object at 0x7ee54ed46f80>"
     },
     "verbose": 1,
     "policy_kwargs": {},
-    "num_timesteps": 1007616,
-    "_total_timesteps": 1000000,
+    "num_timesteps": 16384,
+    "_total_timesteps": 10000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1723477231094352429,
+    "start_time": 1723796299251953997,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -39,22 +39,22 @@
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     "_last_original_obs": {
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     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
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     "_stats_window_size": 100,
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HumanoidStandup-v4/policy.pth

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README.md

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       type: HumanoidStandup-v4
     metrics:
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       name: mean_reward
       verified: false
 ---

results.json

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