Exploitation-Oriented Learning XoL-Reference-Cited by-同舟云学术

Exploitation-Oriented Learning XoL

Published: Issue: Volume: Page:267-293
ISSN:
Container-title:Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies
language:
Short-container-title:

Author:

Miyazaki Kazuteru¹

Affiliation:

1. National Institution for Academic Degrees and University Evaluation, Japan

Abstract

Exploitation-oriented Learning XoL is a new framework of reinforcement learning. XoL aims to learn a rational policy whose expected reward per an action is larger than zero, and does not require a sophisticated design of the value of a reward signal. In this chapter, as examples of learning systems that belongs in XoL, we introduce the rationality theorem of profit Sharing (PS), the rationality theorem of reward sharing in multi-agent PS, and PS-r*. XoL has several features. (1) Though traditional RL systems require appropriate reward and penalty values, XoL only requires an order of importance among them. (2) XoL can learn more quickly since it traces successful experiences very strongly. (3) XoL may be unsuitable for pursuing an optimal policy. The optimal policy can be acquired by the multi-start method that needs to reset all memories to get a better policy. (4) XoL is effective on the classes beyond MDPs, since it is a Bellman-free method that does not depend on DP. We show several numerical examples to confirm these features.

Publisher

IGI Global

Reference27 articles.

1. Abbeel, P., & Ng, A. Y. (2005). Exploration and apprenticeship learning in reinforcement learning. In Proceedings of the Twentyfirst International Conference on Machine Learning (pp. 1-8).

2. Aberdeen, D., & Baxter, J. (2002). Scalable Internal-State Policy-Gradient Methods for POMDPs. In Proceedings of the Nineteenth International Conference on Machine Learning (pp. 3-10).

3. Gradient Descent for General Reinforcement Learning.;L.Baird;Advances in Neural Information Processing Systems,1999

4. Boutilier, C., & Poole, D. (1996). Computing Optimal Policies for Partially Observable Decision Processes using Compact Representations. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 1168-1175).

5. Chrisman, L. (1992). Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach. In Proceedings of the Tenth National Conference on Artificial Intelligence (pp. 183-188).

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Proposal and Evaluation of Deep Profit Sharing Method in a Mixed Reward and Penalty Environment;Studies in Computational Intelligence;2022

2. Classification of Medical Data using Character-level CNN;Proceedings of the 2020 The 3rd International Conference on Information Science and System;2020-03-19

3. Proposal and Evaluation of Detour Path Suppression Method in PS Reinforcement Learning;SICE Journal of Control, Measurement, and System Integration;2019-09-01