Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Cesar514/a2c-PandaReachDense-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Cesar514/a2c-PandaReachDense-v2 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Cesar514/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
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +1 -1
README.md
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@@ -16,7 +16,7 @@ model-index:
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type: PandaReachDense-v2
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metrics:
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- type: mean_reward
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value: -2.
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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: -2.91 +/- 1.02
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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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size 108095
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version https://git-lfs.github.com/spec/v1
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replay.mp4
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results.json
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{"mean_reward": -2.
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{"mean_reward": -2.9118735149502752, "std_reward": 1.0227196933998584, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-01T20:48:57.895752"}
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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:e7f7e7cd22e01edbea7ec4bea93db8c09fd88caae95bf9f17dfb1efe407350a7
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size 3056
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