QRDQN Agent playing BeamRiderNoFrameskip-v4
This is a trained model of a QRDQN agent playing BeamRiderNoFrameskip-v4 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 BeamRiderNoFrameskip-v4 -orga Corianas -f logs/
python enjoy.py --algo qrdqn --env BeamRiderNoFrameskip-v4 -f logs/
Training (with the RL Zoo)
python train.py --algo qrdqn --env BeamRiderNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo qrdqn --env BeamRiderNoFrameskip-v4 -f logs/ -orga Corianas
Hyperparameters
OrderedDict([('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 3),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('normalize', False)])
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Evaluation results
- mean_reward on BeamRiderNoFrameskip-v4self-reported13335.00 +/- 5701.88