Abstract
One of the most prominent approaches for speeding up reinforcement learning is injecting human prior knowledge into the learning agent. This paper proposes a novel method to speed up temporal difference learning by using state-action similarities. These hand-coded similarities are tested in three well-studied domains of varying complexity, demonstrating our approach's benefits.
| Original language | English |
|---|---|
| Title of host publication | 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 |
| Editors | Edmund Durfee, Michael Winikoff, Kate Larson, Sanmay Das |
| Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
| Pages | 1722-1724 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781510855076 |
| State | Published - 2017 |
| Event | 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 - Sao Paulo, Brazil Duration: 8 May 2017 → 12 May 2017 |
Publication series
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| Volume | 3 |
| ISSN (Print) | 1548-8403 |
| ISSN (Electronic) | 1558-2914 |
Conference
| Conference | 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 |
|---|---|
| Country/Territory | Brazil |
| City | Sao Paulo |
| Period | 8/05/17 → 12/05/17 |
Bibliographical note
Publisher Copyright:© Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.