README.md

Query-Policy Misalignment in Preference-Based Reinforcement Learning

This is the official implementation of QPA (Query-Policy Alignment). This codebase is largely originated and modified from BPref.

Brief Introduction

To improve feedback efficiency, most existing PbRL (Preference-based RL) methods focus on selecting queries to maximally improve the overall quality of the reward model, but we find that this may not necessarily lead to improved performance. We identify a long-neglected issue in the existing query selection schemes: Query-Policy Misalignment. This issue can be illustarted by the following figure: the selected segments of some existing query selection schemes typically fall outside the scope of the visitation distribution $d^\pi$ (marked with green circles). Therefore, although these selected segments may improve the overall quality of the reward model in the full state-action space, they contribute little to current policy training and potentially cause feedback inefficiency.

Query-Policy Misalignment can be effectively addressed via policy-aligned query selection and a specially designed hybrid experience replay, which together constitute our method: QPA. Given the limited feedback typically encountered in real-world scenarios, it is preferable to help current policy to get more local guidance using policy-aligned query selection.

Install

conda env create -f conda_env.yml
pip install -e .[docs,tests,extra]
cd custom_dmcontrol
pip install -e .
cd custom_dmc2gym
pip install -e .
pip install git+https://github.com/rlworkgroup/metaworld.git@master#egg=metaworld
pip install pybullet

Benchmark Experiments

The results of DM_Control tasks and Metaworld tasks can be reproduced with the following:

bash run_QPA.sh

Visulization of Learning Curves

You can resort to wandb to login your personal account via export your own wandb api key. Please specify wandb config manually and set 'wandb=true' in 'config/train_QPA.yaml', and

export WANDB_API_KEY=YOUR_WANDB_API_KEY

Bibtex

If you find our code and paper can help, please cite our paper as:

@inproceedings{
hu2023query,
title={Query-policy misalignment in preference-based reinforcement learning},
author={Hu, Xiao and Li, Jianxiong and Zhan, Xianyuan and Jia, Qing-Shan and Zhang, Ya-Qin},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=UoBymIwPJR}
}