We based our code primarily off of ikostrikov's pytorch-rl repo. Follow installation instructions there.
Make sure to install pytorch 0.3.1 (ikostrikov's repo is already using version 0.4.0 - which is incompatible with this code base)
To replicate the mujoco results (with gaussian noise) from the paper you need to run all 750 runs individually with:
python main.py --continuous --use-gaussian-noise --run-index [0-749]
To replicate the mujoco results (with uniform noise) from the paper you need to run all 750 runs individually with:
python main.py --continuous --use-uniform-noise --run-index [0-749]
To replicate the mujoco results (with sparse noise) from the paper you need to run all 750 runs individually with:
python main.py --continuous --use-sparse-noise --run-index [0-749]
To replicate the atari results (with gaussian noise) from the paper you need to run all 270 runs individually with:
python main.py --use-gaussian-noise --run-index [0-269]
To replicate the atari results (with uniform noise) from the paper you need to run all 189 runs individually with:
python main.py --use-uniform-noise --run-index [0-188]
To replicate the atari results (with sparse noise) from the paper you need to run all 189 runs individually with:
python main.py --use-sparse-noise --run-index [0-188]
run visualize.py to visualize performance (requires Visdom)
If you find this useful, please cite our work:
@inproceedings{hendersonromoff2018optimizer,
author = {Joshua Romoff and Peter Henderson and Alexandre Piche and Vincent Francois-Lavet and Joelle Pineau},
title = {Reward Estimation for Variance Reduction in Deep Reinforcement Learning},
booktitle = {Proceedings of the 2nd Annual Conference on Robot Learning(CORL 2018)},
year = {2018}
}
Additionally, if you are relying on the codebase heavily please note the original codebase as well:
@misc{pytorchrl,
author = {Kostrikov, Ilya},
title = {PyTorch Implementations of Reinforcement Learning Algorithms},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/ikostrikov/pytorch-a2c-ppo-acktr}},
}
This repo is CC-BY-NC licensed, as found in the LICENSE file.