Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 284.17 +/- 18.23
+      value: 287.70 +/- 17.53
       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 0x7e1381a8f370>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e1381a8f400>", "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 0x7e1381a8f370>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e1381a8f400>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e1381a8f490>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e1381a8f520>",
-        "_build": "<function ActorCriticPolicy._build at 0x7e1381a8f5b0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7e1381a8f640>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e1381a8f6d0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e1381a8f760>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7e1381a8f7f0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e1381a8f880>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e1381a8f910>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e1381a8f9a0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7e49534827a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e4953482830>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e49534828c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e4953482950>",
+        "_build": "<function ActorCriticPolicy._build at 0x7e49534829e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7e4953482a70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e4953482b00>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e4953482b90>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7e4953482c20>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e4953482cb0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e4953482d40>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e4953482dd0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7e1381a91140>"
+        "_abc_impl": "<_abc._abc_data object at 0x7e495c7dba80>"
     },
     "verbose": 1,
     "policy_kwargs": {},
-    "num_timesteps": 2015232,
-    "_total_timesteps": 2000000,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1696697563526299623,
+    "start_time": 1713550608948612739,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -41,20 +41,20 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.007616000000000067,
+    "_current_progress_remaining": -0.015808000000000044,
     "_stats_window_size": 100,
     "ep_info_buffer": {
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ppo-LunarLander-v2/system_info.txt

@@ -1,9 +1,9 @@
-- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- OS: 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: 2.0.1+cu118
+- PyTorch: 2.2.1+cu121
 - GPU Enabled: True
-- Numpy: 1.23.5
+- Numpy: 1.25.2
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
 - OpenAI Gym: 0.25.2

results.json

@@ -1 +1 @@
-{"mean_reward": 284.17108059868013, "std_reward": 18.234133430834497, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-07T17:31:02.930015"}
+{"mean_reward": 287.6973692703956, "std_reward": 17.53045196811069, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-19T18:32:41.062538"}