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td3-Humanoid-v3-3604187374

Reinforcement Learning
stable-baselines3
Humanoid-v3
deep-reinforcement-learning
Humanoid-v4
Eval Results
Model card Files Community
Quentin Gallouédec commited on
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  1. .gitattributes +1 -0
  2. README.md +75 -0
  3. args.yml +83 -0
  4. config.yml +19 -0
  5. env_kwargs.yml +1 -0
  6. replay.mp4 +3 -0
  7. results.json +1 -0
  8. td3-Humanoid-v3.zip +3 -0
  9. td3-Humanoid-v3/_stable_baselines3_version +1 -0
  10. td3-Humanoid-v3/actor.optimizer.pth +3 -0
  11. td3-Humanoid-v3/critic.optimizer.pth +3 -0
  12. td3-Humanoid-v3/data +121 -0
  13. td3-Humanoid-v3/policy.pth +3 -0
  14. td3-Humanoid-v3/pytorch_variables.pth +3 -0
  15. td3-Humanoid-v3/system_info.txt +7 -0
  16. train_eval_metrics.zip +3 -0
.gitattributes CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - Humanoid-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: TD3
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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: Humanoid-v3
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+ type: Humanoid-v3
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+ metrics:
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+ - type: mean_reward
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+ value: 5597.50 +/- 614.54
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **TD3** Agent playing **Humanoid-v3**
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+ This is a trained model of a **TD3** agent playing **Humanoid-v3**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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+
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+ The RL Zoo is a training framework for Stable Baselines3
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+ reinforcement learning agents,
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+ with hyperparameter optimization and pre-trained agents included.
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+
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+ ## Usage (with SB3 RL Zoo)
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+
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+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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+
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+ Install the RL Zoo (with SB3 and SB3-Contrib):
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+ ```bash
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+ pip install rl_zoo3
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+ ```
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+
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+ ```
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+ # Download model and save it into the logs/ folder
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+ python -m rl_zoo3.load_from_hub --algo td3 --env Humanoid-v3 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo td3 --env Humanoid-v3 -f logs/
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+ ```
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+
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+ If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
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+ ```
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+ python -m rl_zoo3.load_from_hub --algo td3 --env Humanoid-v3 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo td3 --env Humanoid-v3 -f logs/
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+ ```
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+
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+ ## Training (with the RL Zoo)
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+ ```
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+ python -m rl_zoo3.train --algo td3 --env Humanoid-v3 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m rl_zoo3.push_to_hub --algo td3 --env Humanoid-v3 -f logs/ -orga qgallouedec
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+ ```
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+
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+ ## Hyperparameters
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+ ```python
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+ OrderedDict([('batch_size', 256),
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+ ('gradient_steps', 1),
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+ ('learning_rate', 0.0003),
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+ ('learning_starts', 10000),
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+ ('n_timesteps', 2000000.0),
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+ ('noise_std', 0.1),
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+ ('noise_type', 'normal'),
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+ ('policy', 'MlpPolicy'),
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+ ('train_freq', 1),
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+ ('normalize', False)])
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+ ```
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+ - Humanoid-v3
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+ - dummy
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+ - - verbose
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+ - - wandb_entity
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+ - openrlbenchmark
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+ - - wandb_project_name
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+ - sb3
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