Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 264.05 +/- 75.64
+      value: 287.80 +/- 16.52
       name: mean_reward
       verified: false
 ---

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 0x7f0e8535c280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0e8535c310>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0e8535c3a0>", "_build_mlp_extractor": "<function 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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. 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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    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f0e8535c280>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0e8535c310>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0e8535c3a0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0e8535c430>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f0e8535c4c0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f0e8535c550>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0e8535c5e0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f0e8535c670>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0e8535c700>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0e8535c790>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0e8535c820>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f3c32867af0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3c32867b80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3c32867c10>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3c32867ca0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3c32867d30>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3c32867dc0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3c32867e50>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3c32867ee0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3c32867f70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3c3286b040>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3c3286b0d0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f0e8535d0c0>"
+        "_abc_impl": "<_abc_data object at 0x7f3c328653c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -41,48 +41,48 @@
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     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 1230,
-    "n_steps": 1024,
-    "gamma": 0.99,
-    "gae_lambda": 0.95,
-    "ent_coef": 0.0,
+    "_n_updates": 1020,
+    "n_steps": 2048,
+    "gamma": 0.999,
+    "gae_lambda": 0.97,
+    "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 64,

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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