Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 255.28 +/- 23.85
+      value: 297.98 +/- 12.56
       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 0x7fe9dd95b760>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe9dd95b7f0>", "reset_noise": 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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 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 0x7fe9dd95b760>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe9dd95b7f0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe9dd95b880>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe9dd95b910>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fe9dd95b9a0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fe9dd95ba30>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe9dd95bac0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe9dd95bb50>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fe9dd95bbe0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe9dd95bc70>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe9dd95bd00>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe9dd95bd90>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f2569d51a20>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2569d51ab0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2569d51b40>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2569d51bd0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2569d51c60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2569d51cf0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2569d51d80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2569d51e10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2569d51ea0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2569d51f30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2569d51fc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2569d52050>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fe9dd957e00>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f2569d43100>"
     },
     "verbose": 1,
     "policy_kwargs": {},
-    "num_timesteps": 1015808,
-    "_total_timesteps": 1000000.0,
+    "num_timesteps": 4014080,
+    "_total_timesteps": 4000000.0,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1683544215901812713,
+    "start_time": 1683558363193330299,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     "_last_episode_starts": {
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@@ -41,17 +41,17 @@
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     "use_sde": false,
     "sde_sample_freq": -1,
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+    "_current_progress_remaining": -0.0035199999999999676,
     "_stats_window_size": 100,
     "ep_info_buffer": {
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     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 248,
+    "_n_updates": 980,
     "observation_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
         ":serialized:": "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",
@@ -87,13 +87,13 @@
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",
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     },
     "clip_range_vf": null,
     "normalize_advantage": true,
     "target_kl": null,
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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
-- Python: 3.10.11
+- OS: Linux-5.19.0-41-generic-x86_64-with-glibc2.35 # 42~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Apr 18 17:40:00 UTC 2
+- Python: 3.10.6
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.0.0+cu118
+- PyTorch: 2.0.0+cu117
 - GPU Enabled: True
-- Numpy: 1.22.4
+- Numpy: 1.24.3
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
-- OpenAI Gym: 0.25.2
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results.json

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