Baselines for joint-action reinforcement learning of coordination in cooperative multi-agent systems

M Carpenter; D Kudenko

Baselines for joint-action reinforcement learning of coordination in cooperative multi-agent systems

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A common assumption for the study of reinforcement learning of coordination is that agents can observe each other's actions (so-called joint-action learning). We present in this paper a number of simple joint-action learning algorithms and show that they perform very well when compared against more complex approaches such as OAL [1], while still maintaining convergence guarantees. Based on the empirical results, we argue that these simple algorithms should be used as baselines for any future research on joint-action learning of coordination.

Original language	English
Title of host publication	ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS II
Editors	D Kudenko, D Kazakov, E Alonso
Place of Publication	BERLIN
Publisher	Springer
Pages	55-72
Number of pages	18
ISBN (Print)	3-540-25260-6
Publication status	Published - 2005
Event	4th Symposium on Adaptive Agents and Multi-Agent Systems - Leeds Duration: 29 Mar 2004 → 30 Mar 2004

Conference

Conference	4th Symposium on Adaptive Agents and Multi-Agent Systems
City	Leeds
Period	29/03/04 → 30/03/04

Cite this

@inproceedings{c435e51ce2394aaf9c009bad3f1fccf3,

title = "Baselines for joint-action reinforcement learning of coordination in cooperative multi-agent systems",

abstract = "A common assumption for the study of reinforcement learning of coordination is that agents can observe each other's actions (so-called joint-action learning). We present in this paper a number of simple joint-action learning algorithms and show that they perform very well when compared against more complex approaches such as OAL [1], while still maintaining convergence guarantees. Based on the empirical results, we argue that these simple algorithms should be used as baselines for any future research on joint-action learning of coordination.",

author = "M Carpenter and D Kudenko",

year = "2005",

language = "English",

isbn = "3-540-25260-6",

pages = "55--72",

editor = "D Kudenko and D Kazakov and E Alonso",

booktitle = "ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS II",

publisher = "Springer",

address = "Germany",

note = "4th Symposium on Adaptive Agents and Multi-Agent Systems ; Conference date: 29-03-2004 Through 30-03-2004",

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TY - GEN

T1 - Baselines for joint-action reinforcement learning of coordination in cooperative multi-agent systems

AU - Carpenter, M

AU - Kudenko, D

PY - 2005

Y1 - 2005

N2 - A common assumption for the study of reinforcement learning of coordination is that agents can observe each other's actions (so-called joint-action learning). We present in this paper a number of simple joint-action learning algorithms and show that they perform very well when compared against more complex approaches such as OAL [1], while still maintaining convergence guarantees. Based on the empirical results, we argue that these simple algorithms should be used as baselines for any future research on joint-action learning of coordination.

AB - A common assumption for the study of reinforcement learning of coordination is that agents can observe each other's actions (so-called joint-action learning). We present in this paper a number of simple joint-action learning algorithms and show that they perform very well when compared against more complex approaches such as OAL [1], while still maintaining convergence guarantees. Based on the empirical results, we argue that these simple algorithms should be used as baselines for any future research on joint-action learning of coordination.

M3 - Conference contribution

SN - 3-540-25260-6

SP - 55

EP - 72

BT - ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS II

A2 - Kudenko, D

A2 - Kazakov, D

A2 - Alonso, E

PB - Springer

CY - BERLIN

T2 - 4th Symposium on Adaptive Agents and Multi-Agent Systems

Y2 - 29 March 2004 through 30 March 2004

ER -