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// repository documentation

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# Utilizing Skipped Frames in Action Repeats for Improving Sample Efficiency in Reinforcement Learning

##  Description

This repository implements the paper "Utilizing Skipped Frames in Action Repeats for Improving Sample Efficiency in 
Reinforcement Learning".

## Installation

All of the dependencies are in the `conda_env.yml` file. They can be installed manually or with the following command:

```
conda env create -f conda_env.yml
```

## Instructions

Quick start: `bash scripts/cartpole/run.sh`

## Related Work

Hindsight Goal Ranking (https://github.com/tunglm2203/hgr)

## Acknowledgement

This code is implemented on top of [SAC Pytorch](https://github.com/denisyarats/pytorch_sac).

This work was supported in part by the Institute for Information & communications Technology Promotion (IITP) grant funded by the
Korea government (MSIT) (No. 2021-0-01381, Development of Causal AI through Video Understanding) and in part by the Institute of
Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2022-0-00951,
Development of Uncertainty-Aware Agents Learning by Asking Questions).

## BibTex

```
@ARTICLE{9793636,
  author={Luu, Tung M. and Nguyen, Thanh and Vu, Thang and Yoo, Chang D.},
  journal={IEEE Access}, 
  title={Utilizing Skipped Frames in Action Repeats for Improving Sample Efficiency in Reinforcement Learning}, 
  year={2022},
  volume={10},
  number={},
  pages={64965-64975},
  doi={10.1109/ACCESS.2022.3182107}
  }
```

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