Upload Lunar Lander-v2 trained agent

LunarLander_v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7a940828b910>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a940828b9a0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a940828ba30>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a940828bac0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7a940828bb50>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7a940828bbe0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a940828bc70>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a940828bd00>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7a940828bd90>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a940828be20>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a940828beb0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a940828bf40>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7853f338cc10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7853f338cca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7853f338cd30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7853f338cdc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7853f338ce50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7853f338cee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7853f338cf70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7853f338d000>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7853f338d090>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7853f338d120>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7853f338d1b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7853f338d240>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7a9408236f00>"
+        "_abc_impl": "<_abc._abc_data object at 0x7853f3328c80>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -26,12 +26,12 @@
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     "seed": null,
     "action_noise": null,
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-    "learning_rate": 5e-05,
+    "start_time": 1709150348879322746,
+    "learning_rate": 1e-05,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     },
     "_last_episode_starts": {
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@@ -45,13 +45,13 @@
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     "n_steps": 2048,
     "gamma": 0.999,
     "gae_lambda": 0.99,
@@ -94,6 +94,6 @@
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LunarLander_v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9245e25e9a410b1879de9b7f3b4c6bf1d0e7de50ba92aa9d7178bf1898374fce
+oid sha256:e9106c091e462c998dc1c4dbe1acf3c4f5127428597bf5751fbcf65ddda89785
 size 87978

LunarLander_v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f157772816c916122c263ea3a518fa30b4288cdec8d559f89565ab150db33d98
+oid sha256:99db6ba67e5446173795addac68549f566529653550fe53a9e3f120a3956e2f6
 size 43634

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 278.96 +/- 14.00
+      value: 287.87 +/- 25.46
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7a940828b910>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a940828b9a0>", "reset_noise": 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"Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu121", "GPU Enabled": "False", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
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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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7853f338cc10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7853f338cca0>", "reset_noise": 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results.json

@@ -1 +1 @@
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