This is PyTorch implementation of Soft Actor-Critic (SAC) [ArXiv].
If you use this code in your research project please cite us as:
@misc{pytorch_sac,
author = {Yarats, Denis and Kostrikov, Ilya},
title = {Soft Actor-Critic (SAC) implementation in PyTorch},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/denisyarats/pytorch_sac}},
}
We assume you have access to a gpu that can run CUDA 9.2. Then, the simplest way to install all required dependencies is to create an anaconda environment and activate it:
conda env create -f conda_env.yml
source activate pytorch_sac
To train an SAC agent on the cheetah run task run:
python train.py env=cheetah_run
This will produce exp folder, where all the outputs are going to be stored including train/eval logs, tensorboard blobs, and evaluation episode videos. One can attacha tensorboard to monitor training by running:
tensorboard --logdir exp
An extensive benchmarking of SAC on the DM Control Suite against D4PG. We plot an average performance of SAC over 3 seeds together with p95 confidence intervals. Importantly, we keep the hyperparameters fixed across all the tasks. Note that results for D4PG are reported after 10^8 steps and taken from the original paper.
