DQN Agent playing CartPole-v1

This is a trained model of a DQN 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 rl_zoo3.load_from_hub --algo dqn --env CartPole-v1 -orga sb3 -f logs/
python enjoy.py --algo dqn --env CartPole-v1 -f logs/


Training (with the RL Zoo)

python train.py --algo dqn --env CartPole-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env CartPole-v1 -f logs/ -orga sb3


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])'),
('target_update_interval', 10),
('train_freq', 256),
('normalize', False)])