trained model 2e+06 steps

LunarLander-v2-ppo.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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LunarLander-v2-ppo/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    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fbcb39ed560>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbcb39ed5f0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbcb39ed680>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbcb39ed710>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fbcb39ed7a0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fbcb39ed830>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbcb39ed8c0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fbcb39ed950>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbcb39ed9e0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbcb39eda70>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbcb39edb00>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f85272b5200>",
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+        "forward": "<function ActorCriticPolicy.forward at 0x7f85272b53b0>",
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+        "_predict": "<function ActorCriticPolicy._predict at 0x7f85272b54d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f85272b5560>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f85272b55f0>",
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         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fbcb3a37930>"
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     "verbose": 1,
     "policy_kwargs": {},
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"
+        ":serialized:": 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+zsNwQJSGlFKUaBVL4WgWR0CeXuhIe5nUdX2UKGgGaAloD0MIYW9iSM61cECUhpRSlGgVS81oFkdAnl79RekYXXV9lChoBmgJaA9DCJf+JamMGHBAlIaUUpRoFUvnaBZHQJ5fCfFrEcd1fZQoaAZoCWgPQwhP5bSnJOpyQJSGlFKUaBVLwmgWR0CeX1ZIg/1QdX2UKGgGaAloD0MIYhIu5BGJcUCUhpRSlGgVS8toFkdAnl/6XjU/fXV9lChoBmgJaA9DCGX/PA3YqnBAlIaUUpRoFUvVaBZHQJ5hbXoTwlV1fZQoaAZoCWgPQwgDB7R0BQxuQJSGlFKUaBVL6WgWR0CeYWtsvZh8dX2UKGgGaAloD0MIx3+BIMAuckCUhpRSlGgVS/NoFkdAnmH50r9VFXV9lChoBmgJaA9DCHO7l/vkZnJAlIaUUpRoFUvRaBZHQJ5iFJmNBGB1fZQoaAZoCWgPQwgMkdPXs9tyQJSGlFKUaBVLzWgWR0CeYiLFGXoldWUu"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -78,11 +78,11 @@
     "_n_updates": 1230,
     "n_steps": 1024,
     "gamma": 0.999,
-    "gae_lambda": 0.95,
+    "gae_lambda": 0.97,
     "ent_coef": 0.0,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 64,
+    "batch_size": 128,
     "n_epochs": 10,
     "clip_range": {
         ":type:": "<class 'function'>",

LunarLander-v2-ppo/policy.optimizer.pth

@@ -1,3 +1,3 @@
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LunarLander-v2-ppo/policy.pth

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README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 269.09 +/- 65.43
+      value: 275.50 +/- 18.53
       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 0x7fbcb39ed560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbcb39ed5f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbcb39ed680>", "_build_mlp_extractor": "<function 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results.json

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