Pytorch implementations of
- SAC: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
- SAC+AE: Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
- CURL: CURL: Contrastive Unsupervised Representations for Reinforcement Learning
- RAD: Reinforcement Learning with Augmented Data
- DrQ: Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
- ATC: Decoupling Representation Learning from Reinforcement Learning
Install MuJoCo 2.1 if it is not already the case (please refer to https://github.com/deepmind/dm_control.
Create conda environment
conda create -n visualrl -f conda_env.yml
Run the code
conda activate visualrl
python src/train.py --agent drq --domain_name cheetah --task_name run
Here's the benchmark results on PlaNet Benchmark tasks. All results are averaged over 5 different seeds.
Please use the bibtex below if you want to cite this repository in your publications:
@misc{rlcodebase,
author = {Jinwei Xing},
title = {Pytorch Implementations of Reinforcement Learning Algorithms for Visual Continuous Control},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/KarlXing/RL-Visual-Continuous-Control}},
}