11th International Workshop on Cooperative Information Agents Delft, NETHERLANDS, SEP 19-21, 2007, Date: 2007/09/19 - 2007/09/21, Location: NETHERLANDS, Delft
Cooperative Information Agents XI, Proceedings
Author:
Keywords:
automata, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Hardware & Architecture, Computer Science, Information Systems, Computer Science, AUTOMATA
Abstract:
In this paper we compare state-of-the-art multi-agent reinforcement learning algorithms in a wide variety of games. We consider two types of algorithms: value iteration and policy iteration. Four characteristics are studied: initial conditions, parameter settings, convergence speed, and local versus global convergence. Global convergence is still difficult to achieve in practice, despite existing theoretical guarantees. Multiple visualizations are included to provide a comprehensive insight into the learning dynamics.