RL Agent for Lunar lander. n_epochs = 4,

gamma = 0.999,
gae_lambda = 0.98,
ent_coef = 0.01,

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 245.48 +/- 48.97
+      value: 254.44 +/- 27.93
       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 0x7f170e30e7a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f170e30e830>", "reset_noise": 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 - Stable-Baselines3: 2.0.0a5
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@@ -6,3 +6,4 @@
 - Numpy: 1.26.4
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
+- OpenAI Gym: 0.26.2

results.json

@@ -1 +1 @@
-{"mean_reward": 245.48345319999999, "std_reward": 48.96692767228334, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-28T19:28:28.226237"}
+{"mean_reward": 254.4437625, "std_reward": 27.928686032131083, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-08-19T12:11:15.251630"}