Official codebase for Reinforcement Learning with Augmented Data. This codebase was originally forked from rlkit. Official codebases for DM control and Procgen are available at RAD: DM control and RAD: Procgen.
@article{laskin2020reinforcement,
title={Reinforcement learning with augmented data},
author={Laskin, Michael and Lee, Kimin and Stooke, Adam and Pinto, Lerrel and Abbeel, Pieter and Srinivas, Aravind},
journal={arXiv preprint arXiv:2004.14990},
year={2020}
}
- Install and use the included Ananconda environment
$ conda env create -f environment/linux-gpu-env.yml
$ source activate rlkit
You'll need to get your own MuJoCo key if you want to use MuJoCo.
- Add this repo directory to your
PYTHONPATH
environment variable or simply run:
pip install -e .
- Install "benchmarking MBRL",
pip uninstall gym
pip install gym==0.9.4 mujoco-py==0.5.7 termcolor
cd mbbl_envs
pip install --user -e .
SAC
./scripts/run_sac.sh [env_name]
SAC + BN
./scripts/run_sac_bn.sh [env_name]
SAC + Gaussian
./scripts/run_sac_gaussian.sh [env_name] [prob] [std]
SAC + random amplitude scaling
./scripts/run_rand_ampl.sh [env_name] [single_flag: 0 or 1] [equal_flag: 0 or 1] [lower] [upper]