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--- |
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library_name: stable-baselines3 |
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tags: |
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- PandaReachDense-v3 |
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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-v3 |
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type: PandaReachDense-v3 |
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metrics: |
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- type: mean_reward |
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value: -0.20 +/- 0.09 |
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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-v3** |
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This is a trained model of a **A2C** agent playing **PandaReachDense-v3** |
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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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```python |
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from stable_baselines3 import A2C |
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from huggingface_sb3 import load_from_hub |
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model = load_from_hub(repo_id='Francesco-A/a2c-PandaReachDense-v3', |
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filename= 'a2c-PandaReachDense-v3.zip') |
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``` |
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## Training details (last output) |
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Metric | Value |
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---------------------|-------- |
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rollout/ep_len_mean | 4.05 |
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rollout/ep_rew_mean | -0.317 |
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time/fps | 378 |
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time/iterations | 50000 |
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time/time_elapsed | 2641 |
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time/total_timesteps | 1000000 |
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train/entropy_loss | 1.25 |
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train/explained_variance | 0.975 |
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train/learning_rate | 0.0007 |
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train/n_updates | 49999 |
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train/policy_loss | -0.0935 |
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train/std | 0.185 |
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train/value_loss | 0.0306 |
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