appleternity
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Upload PPO LunarLander-v2 trained agent
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +1 -1
- ppo-LunarLander-v2/data +13 -13
- results.json +1 -1
README.md
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 267.46 +/- 16.00
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name: mean_reward
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verified: false
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---
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config.json
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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 0x7f676e8d0430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f676e8d04c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f676e8d0550>", 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18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7ffb26f80a60>",
|
19 |
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7ffb26f6fe40>"
|
21 |
},
|
22 |
"verbose": 1,
|
23 |
"policy_kwargs": {},
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 267.4620575118535, "std_reward": 16.004477702461312, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-01T14:46:41.141707"}
|