second Commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 269.59 +/- 18.80
+      value: 281.21 +/- 24.19
       name: mean_reward
       verified: false
 ---

TaTc29-ppo-LunarLander-v2.zip

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+size 147087

TaTc29-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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-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe06e0e7160>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3094029820>",
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     "seed": null,
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-    "learning_rate": 0.0003,
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"
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     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 310,
-    "n_steps": 2048,
-    "gamma": 0.99,
-    "gae_lambda": 0.95,
-    "ent_coef": 0.0,
+    "_n_updates": 1230,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
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results.json

@@ -1 +1 @@
-{"mean_reward": 269.5902417399799, "std_reward": 18.799164901540244, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-09T15:34:33.311378"}
+{"mean_reward": 281.2051446702815, "std_reward": 24.187363203151033, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-10T10:25:48.002511"}