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MattStammers/qrdqn-SpaceinvadersNoFrameskip-v4-final
qrdqn-SpaceinvadersNoFrameskip-v4-final is a reinforcement learning model from MattStammers. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library and the RL Zoo.
Downloads · 30 days
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From the Hugging Face model README
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-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.
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
pip install rl_zoo3
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
If you installed the RL Zoo3 via pip (pip install rl_zoo3), from anywhere you can do:
python -m rl_zoo3.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga MattStammers -f logs/
python -m rl_zoo3.enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
python -m rl_zoo3.train --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MattStammers
OrderedDict([('batch_size', 64),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_fraction', 0.025),
('frame_stack', 4),
('n_timesteps', 50000000.0),
('normalize', False),
('optimize_memory_usage', False),
('policy', 'CnnPolicy')])
{'render_mode': 'rgb_array'}
So another 24 hours of training has made all the difference and the agent is now ranked number 3. Not because it is getting more points but because it is becoming more consistent