Yunho Choi, Kyungjae Lee, and Songhwai Oh, "Distributional Deep Reinforcement Learning with a Mixture of Gaussians," in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), May 2019.

adapted from https://github.com/Silvicek/distributional-dqn

1. install baseline https://github.com/ececyh/baselines (pip install -e .)
2. pip install gym, box2d (if swig error, sudo apt-get install swig)
3. import train_car_racing and run the exp function ex) train_car_racing.exp(buffer_size=1e6, action_res=[5,5,5])

Implementation of 'A Distributional Perspective on Reinforcement Learning' and 'Distributional Reinforcement Learning with Quantile Regression' based on OpenAi DQN baseline.

Install the OpenAi fork https://github.com/Silvicek/baselines (parent changes a lot, compatibility isn't guaranteed) Then install requirements

pip3 install -r requirements.txt

For simple benchmarking:

python3 train_[{cartpole, pong}].py
python3 enjoy_[{cartpole, pong}].py

For full Atari options see help

python3 train_atari.py --help

after learning, you can visualize the distributions by running

python3 enjoy_atari.py --visual ...

This implementation has been successfully tested on: Pong, Qbert, Seaquest

Some baseline features not supported (prioritized replay, double q-learning, dueling)