Upload PPO LunarLander-v2 trained agent v2

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 269.18 +/- 25.48
+      value: 259.28 +/- 68.13
       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 0x73c750882700>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x73c7508827a0>", "reset_noise": 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"max_grad_norm": 0.5, "rollout_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNgAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwNUm9sbG91dEJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'advantages': <class 'numpy.ndarray'>, 'returns': <class 'numpy.ndarray'>, 'episode_starts': <class 'numpy.ndarray'>, 'log_probs': <class 'numpy.ndarray'>, 'values': <class 'numpy.ndarray'>}", "__doc__": "\n    Rollout buffer used in on-policy algorithms like A2C/PPO.\n    It corresponds to ``buffer_size`` transitions collected\n    using the current policy.\n    This experience will be discarded after the policy update.\n    In order to use PPO objective, we also store the current value of each state\n    and the log probability of each taken action.\n\n    The term rollout here refers to the model-free notion and should not\n    be used with the concept of rollout used in model-based RL or planning.\n    Hence, it is only involved in policy and value function training but not action selection.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n        Equivalent to classic advantage when set to 1.\n    :param gamma: Discount factor\n    :param n_envs: Number of parallel environments\n    ", "__init__": "<function RolloutBuffer.__init__ at 0x73c750a0f2e0>", "reset": "<function RolloutBuffer.reset at 0x73c750a0f380>", "compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x73c750a0f420>", "add": "<function RolloutBuffer.add at 0x73c750a0f560>", "get": "<function RolloutBuffer.get at 0x73c750a0f600>", "_get_samples": "<function 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/data

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@@ -78,9 +78,9 @@
     },
     "n_envs": 16,
     "n_steps": 1024,
-    "gamma": 0.99999,
+    "gamma": 0.999,
     "gae_lambda": 0.98,
-    "ent_coef": 0.005,
+    "ent_coef": 0.02,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "rollout_buffer_class": {
@@ -89,14 +89,14 @@
         "__module__": "stable_baselines3.common.buffers",
         "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'advantages': <class 'numpy.ndarray'>, 'returns': <class 'numpy.ndarray'>, 'episode_starts': <class 'numpy.ndarray'>, 'log_probs': <class 'numpy.ndarray'>, 'values': <class 'numpy.ndarray'>}",
         "__doc__": "\n    Rollout buffer used in on-policy algorithms like A2C/PPO.\n    It corresponds to ``buffer_size`` transitions collected\n    using the current policy.\n    This experience will be discarded after the policy update.\n    In order to use PPO objective, we also store the current value of each state\n    and the log probability of each taken action.\n\n    The term rollout here refers to the model-free notion and should not\n    be used with the concept of rollout used in model-based RL or planning.\n    Hence, it is only involved in policy and value function training but not action selection.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n        Equivalent to classic advantage when set to 1.\n    :param gamma: Discount factor\n    :param n_envs: Number of parallel environments\n    ",
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-        "reset": "<function RolloutBuffer.reset at 0x73c750a0f380>",
-        "compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x73c750a0f420>",
-        "add": "<function RolloutBuffer.add at 0x73c750a0f560>",
-        "get": "<function RolloutBuffer.get at 0x73c750a0f600>",
-        "_get_samples": "<function RolloutBuffer._get_samples at 0x73c750a0f6a0>",
+        "__init__": "<function RolloutBuffer.__init__ at 0x7f860dec72e0>",
+        "reset": "<function RolloutBuffer.reset at 0x7f860dec7380>",
+        "compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x7f860dec7420>",
+        "add": "<function RolloutBuffer.add at 0x7f860dec7560>",
+        "get": "<function RolloutBuffer.get at 0x7f860dec7600>",
+        "_get_samples": "<function RolloutBuffer._get_samples at 0x7f860dec76a0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x73c750a14500>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f860debddc0>"
     },
     "rollout_buffer_kwargs": {},
     "batch_size": 64,

ppo-LunarLander-v2/policy.optimizer.pth

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+oid sha256:cfd26433ca44cc7d9a2f04343b82a541b019729dc8491e958d7ca0adbe5265af
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ppo-LunarLander-v2/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:864018484f6e5c2c97c3f451e301920b7dc68c04f11c265343198cf0081d06a9
+oid sha256:c5adb33cf9e611ac7237a9274e6452759dd4662b8f0282da3e9e383862a84294
 size 43634

results.json

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-{"mean_reward": 269.17943926858925, "std_reward": 25.47957587670742, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-08T18:49:20.566717"}
+{"mean_reward": 259.2777518280756, "std_reward": 68.12628259975511, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-08T19:12:45.269790"}