QRDQN Agent playing CartPole-v1
This is a trained model of a QRDQN agent playing CartPole-v1 using the stable-baselines3 library and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo
SB3: https://github.com/DLR-RM/stable-baselines3
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo qrdqn --env CartPole-v1 -orga jackoyoungblood -f logs/
python enjoy.py --algo qrdqn --env CartPole-v1 -f logs/
Training (with the RL Zoo)
python train.py --algo qrdqn --env CartPole-v1 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo qrdqn --env CartPole-v1 -f logs/ -orga jackoyoungblood
Hyperparameters
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('exploration_final_eps', 0.04),
('exploration_fraction', 0.16),
('gamma', 0.99),
('gradient_steps', 128),
('learning_rate', 0.0023),
('learning_starts', 1000),
('n_timesteps', 50000.0),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[256, 256], n_quantiles=10)'),
('target_update_interval', 10),
('train_freq', 256),
('normalize', False)])
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Evaluation results
- mean_reward on CartPole-v1self-reported500.00 +/- 0.00