Upload ppo-LunarLander-v2/data

ppo-LunarLander-v2/data

@@ -0,0 +1,110 @@
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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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f3ed4957490>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3ed4957520>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3ed49575b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3ed4957640>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3ed49576d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3ed4957760>",
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+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3ed4957880>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3ed4957910>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3ed49579a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3ed4957a30>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f3ed4bf2640>"
+    },
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+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
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+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
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+    "n_steps": 1024,
+    "gamma": 0.99,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.02,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 4096,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
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+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}