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_stable_baselines3_version

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+2.0.0a5

data

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+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
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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 0x7f958defed40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f958defedd0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f958defee60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f958defeef0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f958defef80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f958deff010>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f958deff0a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f958deff130>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f958deff1c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f958deff250>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f958deff2e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f958deff370>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f958defa300>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
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+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1693798153363237430,
+    "learning_rate": 0.0003,
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pytorch_variables.pth

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system_info.txt

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+- OS: Linux-5.15.0-79-generic-x86_64-with-glibc2.35 # 86-Ubuntu SMP Mon Jul 10 16:07:21 UTC 2023
+- Python: 3.10.0
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu117
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1