PabloTa/dqn-SpaceInvadersNoFrameskip-v4

QRDQN Agent playing SpaceInvadersNoFrameskip-v4

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.

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 rl_zoo3.load_from_hub --algo qrdqn --env SpaceInvadersNoFrameskip-v4 -orga PabloTa -f logs/
python enjoy.py --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 PabloTa -f logs/
rl_zoo3 enjoy --algo qrdqn --env SpaceInvadersNoFrameskip-v4  -f logs/

Training (with the RL Zoo)

python train.py --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 PabloTa

Hyperparameters

OrderedDict([('batch_size', 128),
             ('buffer_size', 25000),
             ('env_wrapper',
              ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
             ('exploration_fraction', 0.225),
             ('frame_stack', 3),
             ('learning_rate', 0.023),
             ('n_timesteps', 1000000.0),
             ('normalize', False),
             ('optimize_memory_usage', False),
             ('policy', 'CnnPolicy')])