Reinforcement Learning
stable-baselines3
LunarLander-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use DoggoOP/Lunar_lander with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use DoggoOP/Lunar_lander with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="DoggoOP/Lunar_lander", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Upload PPO LunarLander-v2 trained agent
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +22 -22
- replay.mp4 +0 -0
- 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: -184.98 +/- 31.93
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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 0x7f17fcbde710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f17fcbde7a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f17fcbde830>", 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It allows to keep variance\n above zero and prevent it from growing too fast. 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replay.mp4
CHANGED
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Binary files a/replay.mp4 and b/replay.mp4 differ
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results.json
CHANGED
|
@@ -1 +1 @@
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| 1 |
-
{"mean_reward": -
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{"mean_reward": -184.98119160000002, "std_reward": 31.926750507208464, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-18T04:34:20.475093"}
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