@inproceedings{a07ce4af8c614d83a4485c419cf6ffae,
title = "Speeding up tabular reinforcement learning using state-action similarities",
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.",
author = "Ariel Rosenfeld and Taylor, {Matthew E.} and Sarit Kraus",
note = "Publisher Copyright: {\textcopyright} Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 ; Conference date: 08-05-2017 Through 12-05-2017",
year = "2017",
language = "الإنجليزيّة",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
pages = "1722--1724",
editor = "Edmund Durfee and Michael Winikoff and Kate Larson and Sanmay Das",
booktitle = "16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017",
}