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_stable_baselines3_version

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+1.7.0

data

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+{
+    "policy_class": {
+        ":type:": "<class 'typing.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 0x0000017A6121EEF0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x0000017A6121EF80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x0000017A6121F010>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x0000017A6121F0A0>",
+        "_build": "<function ActorCriticPolicy._build at 0x0000017A6121F130>",
+        "forward": "<function ActorCriticPolicy.forward at 0x0000017A6121F1C0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x0000017A6121F250>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x0000017A6121F2E0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x0000017A6121F370>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x0000017A6121F400>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x0000017A6121F490>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x0000017A6121F520>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x0000017A60005300>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
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+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
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+    },
+    "observation_space": {
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+        "dtype": "float32",
+        "_shape": [
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+        "low": "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]",
+        "high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
+        "bounded_below": "[ True  True  True  True]",
+        "bounded_above": "[ True  True  True  True]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
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+        "n": 2,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 1,
+    "num_timesteps": 1000,
+    "_total_timesteps": 1000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676220973590136300,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
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+    },
+    "_last_obs": {
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+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
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+    "_n_updates": 20000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

policy.optimizer.pth

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+size 40065

policy.pth

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+oid sha256:0fd4e6cb4c4f097c5ba5f721d6ad064f8f53606402800299c51c11c81c35af71
+size 40833

pytorch_variables.pth

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system_info.txt

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+- OS: Windows-10-10.0.19045-SP0 10.0.19045
+- Python: 3.10.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.24.0
+- Gym: 0.21.0