Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 236.42 +/- 21.60
+      value: 274.65 +/- 15.92
       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 0x7ff4910d7dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff4910d7e50>", "reset_noise": 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ppo-LunarLander-v2.zip

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-oid sha256:5cda00148f79489b924d4f24d44c3a87170c8aae73d4381e4e8cbc24f90e3633
+oid sha256:7cd1c6930611a30657518fe8fc6480c0e0277e9b40888055fa7ee68f8ae2e941
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ppo-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    ",
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-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff4910d7e50>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff4910d7ee0>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff4910d7f70>",
-        "_build": "<function ActorCriticPolicy._build at 0x7ff49105b040>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7ff49105b0d0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff49105b160>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff49105b1f0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7ff49105b280>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff49105b310>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff49105b3a0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff49105b430>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f8e3fc7ac10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8e3fc7aca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8e3fc7ad30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8e3fc7adc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8e3fc7ae50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8e3fc7aee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8e3fc7af70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8e3fc7e040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8e3fc7e0d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8e3fc7e160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8e3fc7e1f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8e3fc7e280>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7ff4910ceea0>"
+        "_abc_impl": "<_abc_data object at 0x7f8e3fc74b40>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -43,12 +43,12 @@
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     "n_envs": 16,
-    "num_timesteps": 1015808,
-    "_total_timesteps": 1000000,
+    "num_timesteps": 1212416,
+    "_total_timesteps": 1200000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1673426543513760210,
+    "start_time": 1673484036447477209,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,7 +57,7 @@
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@@ -67,16 +67,16 @@
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     "sde_sample_freq": -1,
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+    "_current_progress_remaining": -0.010346666666666726,
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ppo-LunarLander-v2/system_info.txt

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 - OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
 - Python: 3.8.16
 - Stable-Baselines3: 1.7.0
-- PyTorch: 1.13.0+cu116
+- PyTorch: 1.13.1+cu116
 - GPU Enabled: True
 - Numpy: 1.21.6
 - Gym: 0.21.0

results.json

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