Upload PPO LunarLander-v2 LunarLander-v2_PPO_ne16_ns1024_b64_e4_cpu_TotalStep14000K.zip

LunarLander-v2_PPO_ne16_ns1024_b64_e4_cpu_TotalStep14000K.zip

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LunarLander-v2_PPO_ne16_ns1024_b64_e4_cpu_TotalStep14000K/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 0x7ff7606941f0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff760694280>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff760694310>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff7606943a0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7ff760694430>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7ff7606944c0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff760694550>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff7606945e0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7ff760694670>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff760694700>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff760694790>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff760694820>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f2803f51160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2803f511f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2803f51280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2803f51310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2803f513a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2803f51430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2803f514c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2803f51550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2803f515e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2803f51670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2803f51700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2803f51790>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7ff7606f3d20>"
+        "_abc_impl": "<_abc_data object at 0x7f2803fb2d20>"
     },
     "verbose": 0,
     "policy_kwargs": {},
@@ -26,12 +26,12 @@
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     "action_noise": null,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
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LunarLander-v2_PPO_ne16_ns1024_b64_e4_cpu_TotalStep14000K/policy.optimizer.pth

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 version https://git-lfs.github.com/spec/v1
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+oid sha256:849d3d12b2304968fd58d6e3950def05e2d14ceb3e014ee795632767cd1fafb5
 size 87978

LunarLander-v2_PPO_ne16_ns1024_b64_e4_cpu_TotalStep14000K/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:7a41a488dada2cff72a430207c0333d6e4272b7f34ae63c390ea85aefc8b8b1d
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README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 298.45 +/- 18.92
+      value: 288.93 +/- 18.53
       name: mean_reward
       verified: false
 ---

config.json

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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 0x7ff7606941f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff760694280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff760694310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff7606943a0>", "_build": "<function ActorCriticPolicy._build at 0x7ff760694430>", "forward": "<function ActorCriticPolicy.forward at 0x7ff7606944c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff760694550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff7606945e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff760694670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff760694700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff760694790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff760694820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff7606f3d20>"}, "verbose": 0, "policy_kwargs": {}, "num_timesteps": 14008320, "_total_timesteps": 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