Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use eswat/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use eswat/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="eswat/a2c-PandaReachDense-v2", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Initial commit
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +1 -1
- results.json +1 -1
- vec_normalize.pkl +1 -1
README.md
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type: PandaReachDense-v2
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metrics:
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- type: mean_reward
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value: -3.
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name: mean_reward
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verified: false
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---
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type: PandaReachDense-v2
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metrics:
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- type: mean_reward
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value: -3.95 +/- 0.87
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name: mean_reward
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verified: false
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---
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a2c-PandaReachDense-v2.zip
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version https://git-lfs.github.com/spec/v1
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results.json
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{"mean_reward": -3.
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{"mean_reward": -3.9500575168058276, "std_reward": 0.8657948690374899, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-05T11:07:47.526104"}
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vec_normalize.pkl
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size 3056
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version https://git-lfs.github.com/spec/v1
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oid sha256:26852de821dc280618cf60532cc060ce57c27db34ffd3f245f0c9845fb898e32
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size 3056
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