Initial model

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 270.46 +/- 22.60
+      value: 277.47 +/- 26.59
       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 0x7fb609d5b400>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb609d5b490>", "reset_noise": 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"Linux-6.1.11-100.fc36.x86_64-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Feb  9 20:36:30 UTC 2023", "Python": "3.10.9", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.2", "Gym": "0.21.0"}}
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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 0x7fd93ba4f400>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd93ba4f490>", "reset_noise": 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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 0x7fb609d5b400>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb609d5b490>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb609d5b520>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb609d5b5b0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fb609d5b640>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fb609d5b6d0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb609d5b760>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb609d5b7f0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fb609d5b880>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb609d5b910>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb609d5b9a0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb609d5ba30>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fd93ba4f400>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd93ba4f490>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd93ba4f520>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd93ba4f5b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fd93ba4f640>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fd93ba4f6d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd93ba4f760>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd93ba4f7f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fd93ba4f880>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd93ba4f910>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd93ba4f9a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd93ba4fa30>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fb609d56600>"
+        "_abc_impl": "<_abc._abc_data object at 0x7fd93ba51c80>"
     },
     "verbose": 1,
     "policy_kwargs": {},

results.json

@@ -1 +1 @@
-{"mean_reward": 270.45532373390023, "std_reward": 22.59524982674774, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-24T11:55:21.489342"}
+{"mean_reward": 277.47112944186, "std_reward": 26.591552231896348, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-24T16:06:08.006601"}