PPO LunarLander-v2 trained agent 500k steps

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 285.17 +/- 22.02
+      value: 275.34 +/- 14.56
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
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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 0x7eff23f32290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eff23f32320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eff23f323b0>", "_build_mlp_extractor": "<function 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"
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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -80,8 +80,8 @@
     "gamma": 0.999,
     "gae_lambda": 0.98,
     "ent_coef": 0.01,
-    "vf_coef": 0.5,
-    "max_grad_norm": 0.5,
+    "vf_coef": 0.1,
+    "max_grad_norm": 0.1,
     "batch_size": 16,
     "n_epochs": 4,
     "clip_range": {

lunar_model/policy.optimizer.pth

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 version https://git-lfs.github.com/spec/v1
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+        "__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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ppo-LunarLander-v2/policy.optimizer.pth

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results.json

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