Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 213.01 +/- 65.75
+      value: 258.67 +/- 18.68
       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 0x13b710860>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x13b710900>", "reset_noise": "<function 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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 0x144a10860>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x144a10900>", "reset_noise": "<function 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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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+        "__init__": "<function ActorCriticPolicy.__init__ at 0x144a10860>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x144a10900>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x144a109a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x144a10a40>",
+        "_build": "<function ActorCriticPolicy._build at 0x144a10ae0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x144a10b80>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x144a10c20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x144a10cc0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x144a10d60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x144a10e00>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x144a10ea0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x144a10f40>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x13b70a200>"
+        "_abc_impl": "<_abc._abc_data object at 0x142814cc0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
-    "num_timesteps": 1000448,
+    "num_timesteps": 1001472,
     "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1710645272375109000,
+    "start_time": 1710645748747785000,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "_last_obs": {
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     "_last_episode_starts": {
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@@ -41,17 +41,17 @@
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     "sde_sample_freq": -1,
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@@ -77,14 +77,14 @@
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     },
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+    "n_steps": 2048,
     "gamma": 0.999,
-    "gae_lambda": 0.98,
+    "gae_lambda": 0.99,
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 64,
-    "n_epochs": 4,
+    "n_epochs": 10,
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results.json

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