Sciweavers

Share
AAAI
1998

The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems

8 years 7 months ago
The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems
Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multiagent systems. We examine some of the factors that can influence the dynamics of the learning process in such a setting. We first distinguish reinforcement learners that are unaware of (or ignore) the presence of other agents from those that explicitly attempt to learn the value of joint actions and the strategies of their counterparts. We study (a simple form of) Q-learning in cooperative multiagent systems under these two perspectives, focusing on the influence of that game structure and exploration strategies on convergence to (optimal and suboptimal) Nash equilibria. We then propose alternative optimistic exploration strategies that increase the likelihood of convergence to an optimal equilibrium.
Caroline Claus, Craig Boutilier
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where AAAI
Authors Caroline Claus, Craig Boutilier
Comments (0)
books