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PyTorch implementation of Soft Actor-Critic

PyTorch implementation of Soft Actor-Critic

pytorch_sac

Soft Actor-Critic (SAC) implementation in PyTorch
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}},
}

Requirements

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

Instructions

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

Results

An extensive benchmarking of SAC on the DM Control Suite against D4PG. We plot an average performance of SAC over 5 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.

dm_control

GitHub

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