This repository contains the code for mulit-step greedy reinforcement learning algorithms. It mainly includes two variants, discrete action case (DQN) and continous action case (TRPO), based on the paper Multi-step Greedy Reinforcement Learning Algorithms, which was recently presented at ICML 2020.

This implementation makes use of Tensorflow and builds over the code provided by stable-baselines.

All dependencies are provided in a python virtual-env requirements.txt file. Majorly, you would need to install stable-baselines, tensorflow, and mujoco_py.

1. Install stable-baselines

pip install stable-baselines[mpi]==2.7.0

2. Download and copy MuJoCo library and license files into a .mujoco/ directory. We use mujoco200 for this project.

3. Clone this repository and copy the deepq_kpi, deepq_kvi, trpo_kpi, and trpo-kvi directories inside this directory.

4. Activate virtual-env using the requirements.txt file provided.

source <virtual env path>/bin/activate

Use the run_atari.py and run_mujoco.py script for training the kappa-PI/VI variants for DQN and TRPO respectively.

Kappa-PI/VI DQN

python3 run_atari.py --env=BreakoutNoFrameskip-v4

Kappa-PI/VI TRPO

python3 run_mujoco.py --env=Walker2d-v2 

@inproceedings{tomar2020multi,
  title={Multi-step Greedy Reinforcement Learning Algorithms},
  author={Tomar, Manan and Efroni, Yonathan and Ghavamzadeh, Mohammad},
  booktitle={International Conference on Machine Learning},
  pages={9504--9513},
  year={2020},
  organization={PMLR}
}