Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -7.79 +/- 2.10
+      value: -1.35 +/- 0.25
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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a2c-PandaReachDense-v2/data

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     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 100000,
-    "n_steps": 5,
+    "_n_updates": 12500,
+    "n_steps": 20,
     "gamma": 0.99,
     "gae_lambda": 1.0,
-    "ent_coef": 0.0,
+    "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
     "normalize_advantage": false,

a2c-PandaReachDense-v2/policy.optimizer.pth

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 version https://git-lfs.github.com/spec/v1
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a2c-PandaReachDense-v2/policy.pth

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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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7fcd905e01f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fce3809e300>"}, "verbose": 1, "policy_kwargs": 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