https://github.com/mishalaskin/goal_sac
https://github.com/mishalaskin/goal_sac
Last synced: 24 days ago
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- Host: GitHub
- URL: https://github.com/mishalaskin/goal_sac
- Owner: MishaLaskin
- Created: 2020-05-15T15:41:19.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-10-28T04:31:38.000Z (about 5 years ago)
- Last Synced: 2025-01-23T06:45:09.909Z (9 months ago)
- Language: Python
- Size: 9.38 MB
- Stars: 3
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Soft Actor-Critic (SAC) implementation in PyTorch
This is PyTorch implementation of Soft Actor-Critic (SAC) [[ArXiv]](https://arxiv.org/abs/1812.05905).
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.
