This repository serves to open-source the code used in the paper: "Look Back When Surprised: Stabilizing Reverse Experience Replay for Neural Approximation".
To avoid any conflict with your existing Python setup, it is suggested to work in a virtual environment with virtualenv
. To install virtualenv
:
pip install --upgrade virtualenv
Create a virtual environment, activate it and install the requirements in requirements.txt
.
virtualenv env
source env/bin/activate
pip install -r requirements.txt
Our models can be trained as follows:
python -W ignore -m src.main --exp_name <name> --algo <algo> --replay_buffer_sampler <replay_buffer_choice> --env <env_name> --train --seed $1 --snapshot_dir $2
If you find our codes useful, do consider citing our paper:
@article{kumar2022look,
title={Look Back When Surprised: Stabilizing Reverse Experience Replay for Neural Approximation},
author={Kumar, Ramnath and Nagaraj, Dheeraj},
journal={arXiv preprint arXiv:2206.03171},
year={2022}
}
Our repository makes use of various open-source codes. Most of which have been documented at Garage. If you find the respective codes useful, do cite their respective papers as well:
@misc{garage,
author = {The garage contributors},
title = {Garage: A toolkit for reproducible reinforcement learning research},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/rlworkgroup/garage}},
commit = {be070842071f736eb24f28e4b902a9f144f5c97b}
}
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