Upload PPO LunarLander-v2 trained agent

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 231.54 +/- 86.82
+      value: 285.35 +/- 17.49
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f78650ddb00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f78650ddb90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78650ddc20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78650ddcb0>", "_build": "<function ActorCriticPolicy._build at 0x7f78650ddd40>", "forward": "<function ActorCriticPolicy.forward at 0x7f78650dddd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78650dde60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f78650ddef0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78650ddf80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78650dc050>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78650dc0e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f78650af6c0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": 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ppo-LunarLander-v2.zip

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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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f78650ddb90>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78650ddc20>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78650ddcb0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f78650ddd40>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f78650dddd0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78650dde60>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f78650ddef0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78650ddf80>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78650dc050>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78650dc0e0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fbb17d50950>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbb17d509e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbb17d50a70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbb17d50b00>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbb17d50b90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbb17d50c20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbb17d50cb0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbb17d50d40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbb17d50dd0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbb17d50e60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbb17d50ef0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f78650af6c0>"
+        "_abc_impl": "<_abc_data object at 0x7fbb17d9da50>"
     },
     "verbose": 1,
     "policy_kwargs": {},
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         "high": "[inf inf inf inf inf inf inf inf]",
         "bounded_below": "[False False False False False False False False]",
         "bounded_above": "[False False False False False False False False]",
-        "_np_random": "RandomState(MT19937)"
+        "_np_random": null
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     "action_space": {
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         "_shape": [],
         "dtype": "int64",
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     },
-    "n_envs": 1,
-    "num_timesteps": 1000448,
-    "_total_timesteps": 1000000,
+    "n_envs": 16,
+    "num_timesteps": 2015232,
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     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1658522874.2335336,
+    "start_time": 1658574189.942692,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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 Python: 3.7.13
 Stable-Baselines3: 1.6.0
 PyTorch: 1.12.0+cu113
-GPU Enabled: True
+GPU Enabled: False
 Numpy: 1.21.6
 Gym: 0.21.0

results.json

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