bigger and better model

PPO-MlpPolicy-LunarLander-v2.zip

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PPO-MlpPolicy-LunarLander-v2/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 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    ",
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         "__abstractmethods__": "frozenset()",
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"
+        ":serialized:": 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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -82,7 +101,7 @@
     "ent_coef": 0.0,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 256,
+    "batch_size": 2048,
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PPO-MlpPolicy-LunarLander-v2/policy.optimizer.pth

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PPO-MlpPolicy-LunarLander-v2/policy.pth

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README.md

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   results:
   - metrics:
     - type: mean_reward
-      value: 184.07 +/- 69.58
+      value: 284.52 +/- 16.29
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

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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 0x7fe6110570e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe611057170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe611057200>", "_build_mlp_extractor": "<function 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replay.mp4

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-size 205246
+oid sha256:c7177274cf95b706be25cee9b48e911aea2285376e745f08ae4b755e6868bdca
+size 198659

results.json

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