Initial training for PandaReachDense-v2
Browse files- README.md +37 -0
- a2c-PandaReachDense-v2.zip +3 -0
- a2c-PandaReachDense-v2/_stable_baselines3_version +1 -0
- a2c-PandaReachDense-v2/data +94 -0
- a2c-PandaReachDense-v2/policy.optimizer.pth +3 -0
- a2c-PandaReachDense-v2/policy.pth +3 -0
- a2c-PandaReachDense-v2/pytorch_variables.pth +3 -0
- a2c-PandaReachDense-v2/system_info.txt +7 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
README.md
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---
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library_name: stable-baselines3
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tags:
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- PandaReachDense-v2
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: A2C
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: PandaReachDense-v2
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type: PandaReachDense-v2
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metrics:
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- type: mean_reward
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value: -2.47 +/- 0.66
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name: mean_reward
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verified: false
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---
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# **A2C** Agent playing **PandaReachDense-v2**
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This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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TODO: Add your code
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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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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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oid sha256:b7568915ba83974bd175e44a7a4c0dd4e4db0ce40b7466f3a730acd1a32643b3
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size 108107
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a2c-PandaReachDense-v2/_stable_baselines3_version
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1.7.0
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a2c-PandaReachDense-v2/data
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{
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"__module__": "stable_baselines3.common.policies",
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