Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -166.75 +/- 18.65
+      value: 213.40 +/- 79.12
       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 0x7f2d0ba06170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2d0ba06200>", "reset_noise": 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"Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "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. 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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 0x7f2d0ba06170>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2d0ba06200>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2d0ba06290>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2d0ba06320>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f2d0ba063b0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f2d0ba06440>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2d0ba064d0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2d0ba06560>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f2d0ba065f0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2d0ba06680>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2d0ba06710>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2d0ba067a0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7b90846ec4c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b90846ec550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b90846ec5e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b90846ec670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b90846ec700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b90846ec790>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b90846ec820>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b90846ec8b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b90846ec940>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b90846ec9d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b90846eca60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b90846ecaf0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f2d0b9b7c80>"
+        "_abc_impl": "<_abc._abc_data object at 0x7b908487fa00>"
     },
     "verbose": 1,
     "policy_kwargs": {},
-    "num_timesteps": 100352,
-    "_total_timesteps": 100000,
+    "num_timesteps": 500736,
+    "_total_timesteps": 500000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1718987887553194843,
+    "start_time": 1719078420477679435,
     "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,
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+    "_current_progress_remaining": -0.0014719999999999178,
     "_stats_window_size": 100,
     "ep_info_buffer": {
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