Market-Based Reinforcement Learning in Partially Observable Worlds
人工智能
2007-05-23 v1 机器学习
多智能体系统
神经与进化计算
摘要
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an agent needs to learn short-term memories of relevant previous events in order to execute optimal actions. Most previous work, however, has focused on reactive settings (MDPs) instead of POMDPs. Here we reimplement a recent approach to market-based RL and for the first time evaluate it in a toy POMDP setting.
引用
@article{arxiv.cs/0105025,
title = {Market-Based Reinforcement Learning in Partially Observable Worlds},
author = {Ivo Kwee and Marcus Hutter and Juergen Schmidhuber},
journal= {arXiv preprint arXiv:cs/0105025},
year = {2007}
}
备注
8 LaTeX pages, 2 postscript figures