land it better

LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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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 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 0x7cb86d1441f0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7cb86d144280>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7cb86d144310>",
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-        "_build": "<function ActorCriticPolicy._build at 0x7cb86d144430>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7cb86d1444c0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7cb86d144550>",
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-        "_predict": "<function ActorCriticPolicy._predict at 0x7cb86d144670>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7cb86d144700>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7cb86d144790>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7cb86d144820>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7a3ac5120700>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a3ac5120820>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a3ac51208b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7a3ac5120940>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7a3ac51209d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a3ac5120a60>",
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+        "_predict": "<function ActorCriticPolicy._predict at 0x7a3ac5120b80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a3ac5120c10>",
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+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a3ac5120d30>",
         "__abstractmethods__": "frozenset()",
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+        "_abc_impl": "<_abc._abc_data object at 0x7a3ac50bb100>"
     },
     "verbose": 1,
     "policy_kwargs": {},
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     "tensorboard_log": null,
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     "n_envs": 16,
     "n_steps": 1024,
-    "gamma": 0.995,
+    "gamma": 1,
     "gae_lambda": 0.99,
-    "ent_coef": 0.1,
+    "ent_coef": 0.05,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "batch_size": 64,

LunarLander-v2/policy.optimizer.pth

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-size 87929
+oid sha256:1bcf5227a9c2186f90f659579750d2576afd36aa43f7ef8e891c1e51d57f8a7c
+size 87545

LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8a1fd0a7aab8c8f83d5e98e61fa3ee24de780d11c38efcac730a4e6a48af66a0
-size 43329
+oid sha256:c4650ba102f2277041224f6cb034375336a4a24c511bd00d6597b789be712b37
+size 43201

LunarLander-v2/system_info.txt

@@ -2,7 +2,7 @@
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.0.1+cu118
-- GPU Enabled: True
+- GPU Enabled: False
 - Numpy: 1.23.5
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 193.05 +/- 74.94
+      value: 213.27 +/- 93.28
       name: mean_reward
       verified: false
 ---

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 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 0x7cb86d1441f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7cb86d144280>", "reset_noise": 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results.json

@@ -1 +1 @@
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