Publications on Distributed and Multi-Agent RL

Dowling, Jim , Eoin Curran, Raymond Cunningham, Vinny Cahill (
Using Feedback in Collaborative Reinforcement Learning to Adaptively Optimise MANET Routing
IEEE Transactions on Systems, Man and Cybernetics (Part A), Special Issue on Engineering Self-Orangized Distributed Systems, vol. 35, no. 3, pages 360-372, May 2005.
Abstract: This work describes a decentralized, multi-agent learning algorithm, called collaborative reinforcement learning and demonstrates how it can be used to optimize the system properties (e.g., throughput) of a routing algorithm for Mobile Ad Hoc Networks (MANETs).

Dowling, Jim , Seif Haridi (
Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems
Chapter in Reinforcement Learning: Theory and Applications, Advanced Robotic Systems Journal, Editors Cornelius Weber, Mark Elshaw and Norbert Michael Mayer. I-Tech Education and Publishing, ISBN 978-3-902613-14-1, 2008: 142-167.

Goldberg, Dani , Maja Mataric(
Coordinating Mobile Robot Group Behavior Using a Model of Interaction Dynamics
unpublished ( gzipped Postscript - 263 KB) Abstract:
In this paper we show how various levels of coordinated behavior may be achieved in a group of mob...

Leslie, David , E. J. Collins
Individual Q-learning in normal form games
unpublished (PDF - 210K) Abstract:
The single-agent multi-armed bandit problem can be solved by an agent that learns the values of e...

Strens, Malcolm
Learning Multi-Agent Search Strategies
The Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour, 1(4), 2003. (pdf - 305KB) Abstract:
We identify a specialised class of reinforcement learning problem in which the agent(s) have the goa...

Tambe, Milind , Jafar Adibi, Yaser Al-Onaizan, Ali Erdem, Gal Kaminaka, Stacy Marsella, Ion Muslea, Marcello Tallis(
Building Agent Teams Using an Explicit Teamwork Model and Learning
Artificial Intelligence 1999 (Postscript - 790 KB) Abstract:
Multi-agent collaboration or teamwork and learning are two critical research challenges in a large ...

Tangamchit, Poj , John M. Dolan, Pradeep K. Khosla
The Necessity of Average Rewards in Cooperative Multirobot Learning
ICRA 2002 (pdf - 176KB) Abstract:
Learning can be an effective way for robot systems to deal with dynamic environments and changing ta...

Tumer, Kagan , David Wolpert(
Collective Intelligence and Braess' Paradox
To appear in AAAI 2000 in Austin, Tx (Postscript - 330 KB) Abstract:
We consider the use of multi-agent systems to control network routing. Conventional approaches to ...

Veloso, Manuela , William Uther, Masahiro Fujita, Minoru Asada, Hiroaki Kitano(
Playing Soccer with Legged Robots
Proceedings of IROS-98, Intelligent Robots and Systems Conference ( PDF - 278 KB) Abstract:
Sony has provided a remarkable platform for research and development in robotic agents, namely fully...

Wang, Gang , Sridhar Mahadevan(
Hierarchical Optimization of Policy-Coupled Semi-Markov Decision Processes
International Conference on Machine Learning (ICML-99) ( gzipped Postscript - 250) Abstract:
Manufacturing is a challenging real-world domain for applying MDP-based reinforcement learning algo...

Wolpert, David , Kagan Tumer(
An introduction to Collective Intelligence
NASA tech rep NASA-ARC-IC-99-63, (to appear in J. M. Bradshaw, ed, handbook on agent technology) (Postscript - 1.4 MB) Abstract:
This paper introduces the concept of ``COllective INtelligence'' (COIN). A COIN is a large multi-a...

Wolpert, David , Kevin Wheeler and Kagan Tumer(
Collective Intelligence for Control of Distributed Dynamical Systems
Europhysics Letters , Vol. 49, No. 6, March 2000. (Postscript - 350KB) Abstract:
We consider the El Farol bar problem (W. B. Arthur, The American Economic Review , 84(2): 406--411 (...

preux, philippe , samuel delepoulle, jean-claude darcheville
A Generic architecture for Adaptive Agents Based on Reinforcement Learning
Information Sciences Journal Abstract:
In this paper, we present MAABAC, a generic model for building adaptive agents: they learn new behav...