third trained model upload

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 272.01 +/- 14.20
+      value: 275.76 +/- 26.02
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
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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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f5f04092440>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5f040924d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5f04092560>", "_build_mlp_extractor": "<function 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GaAloD0MIJAwDlpzhckCUhpRSlGgVS+JoFkdArB4PgWJrL3V9lChoBmgJaA9DCNZSQNo/nnFAlIaUUpRoFUvQaBZHQKweO1NQCS11fZQoaAZoCWgPQwikb9I06ANwQJSGlFKUaBVL3WgWR0CsHk+XJHRUdX2UKGgGaAloD0MI6brwg7OccUCUhpRSlGgVS/5oFkdArB5oj4YaYXV9lChoBmgJaA9DCCy4H/CASnJAlIaUUpRoFU0PAWgWR0CsHoOIqLCOdX2UKGgGaAloD0MIEJVGzGzMcUCUhpRSlGgVS9loFkdArB7J5eJHiHV9lChoBmgJaA9DCNDtJY2RbXBAlIaUUpRoFUvQaBZHQKwfCkrwvxp1fZQoaAZoCWgPQwiYE7TJ4VRxQJSGlFKUaBVNCQFoFkdArB8mZ5Rj0HV9lChoBmgJaA9DCLTmx1+adnBAlIaUUpRoFUvnaBZHQKwgIWZ7Xxx1fZQoaAZoCWgPQwiJCtXNReVzQJSGlFKUaBVL5mgWR0CsIDTYdyT7dWUu"
+        ":serialized:": 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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -79,10 +79,10 @@
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,
-    "ent_coef": 0.02,
+    "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 64,
+    "batch_size": 128,
     "n_epochs": 8,
     "clip_range": {
         ":type:": "<class 'function'>",

ppo-LunarLander-v0/policy.optimizer.pth

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results.json

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