UAI 2023poster4 citations
Energy-based Predictive Representations for Partially Observed Reinforcement Learning
Tianjun Zhang, Tongzheng Ren, Chenjun Xiao, Wenli Xiao, Joseph E. Gonzalez, Dale Schuurmans, Bo Dai
Abstract
In real-world applications, handling partial observability is a common requirement for reinforcement learning algorithms, which is not captured by a Markov decision process (MDP). Although partially observable Markov decision processes (POMDPs) have been specifically designed to address this requirement, they present significant computational and statistical challenges in learning and planning. In this work, we introduce the
BibTeX
@InProceedings{pmlr-v216-zhang23b,
title = {Energy-based Predictive Representations for Partially Observed Reinforcement Learning},
author = {Zhang, Tianjun and Ren, Tongzheng and Xiao, Chenjun and Xiao, Wenli and Gonzalez, Joseph E. and Schuurmans, Dale and Dai, Bo},
booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence},
pages = {2477--2487},
year = {2023},
editor = {Evans, Robin J. and Shpitser, Ilya},
volume = {216},
series = {Proceedings of Machine Learning Research},
month = {31 Jul--04 Aug},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v216/zhang23b/zhang23b.pdf},
url = {https://proceedings.mlr.press/v216/zhang23b.html},
abstract = {In real-world applications, handling partial observability is a common requirement for reinforcement learning algorithms, which is not captured by a Markov decision process (MDP). Although partially observable Markov decision processes (POMDPs) have been specifically designed to address this requirement, they present significant computational and statistical challenges in learning and planning. In this work, we introduce the