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qrdqn-Acrobot-v1-2309622469

Reinforcement Learning
stable-baselines3
Acrobot-v1
deep-reinforcement-learning
Eval Results
Model card Files Community
Quentin Gallouédec commited on
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  1. .gitattributes +1 -0
  2. README.md +79 -0
  3. args.yml +79 -0
  4. config.yml +27 -0
  5. env_kwargs.yml +1 -0
  6. qrdqn-Acrobot-v1.zip +3 -0
  7. qrdqn-Acrobot-v1/_stable_baselines3_version +1 -0
  8. qrdqn-Acrobot-v1/data +129 -0
  9. qrdqn-Acrobot-v1/policy.optimizer.pth +3 -0
  10. qrdqn-Acrobot-v1/policy.pth +3 -0
  11. qrdqn-Acrobot-v1/pytorch_variables.pth +3 -0
  12. qrdqn-Acrobot-v1/system_info.txt +7 -0
  13. replay.mp4 +3 -0
  14. results.json +1 -0
  15. 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
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - Acrobot-v1
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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: QRDQN
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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: Acrobot-v1
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+ type: Acrobot-v1
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+ metrics:
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+ - type: mean_reward
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+ value: -86.40 +/- 23.60
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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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+ # **QRDQN** Agent playing **Acrobot-v1**
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+ This is a trained model of a **QRDQN** agent playing **Acrobot-v1**
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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 qrdqn --env Acrobot-v1 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo qrdqn --env Acrobot-v1 -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 qrdqn --env Acrobot-v1 -orga qgallouedec -f logs/
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+ python -m rl_zoo3.enjoy --algo qrdqn --env Acrobot-v1 -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 qrdqn --env Acrobot-v1 -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 qrdqn --env Acrobot-v1 -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', 128),
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+ ('buffer_size', 50000),
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+ ('exploration_final_eps', 0.1),
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+ ('exploration_fraction', 0.12),
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+ ('gamma', 0.99),
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+ ('gradient_steps', -1),
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+ ('learning_rate', 0.00063),
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+ ('learning_starts', 0),
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+ ('n_timesteps', 100000.0),
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+ ('policy', 'MlpPolicy'),
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+ ('policy_kwargs', 'dict(net_arch=[256, 256], n_quantiles=25)'),
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+ ('target_update_interval', 250),
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+ ('train_freq', 4),
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+ ('normalize', False)])
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+ ```
args.yml ADDED
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+ - - target_update_interval
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+ - 250
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+ - - train_freq
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+ - 4
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