Multiagent learning in the presence of agents with limitations
- Michael Bowling, University of Alberta
Learning to act in a multiagent environment is a challenging problem. Optimal behavior for one agent depends upon the behavior of the other agents, which are learning as well. Multiagent environments are therefore non-stationary, violating the traditional assumption underlying single-agent learning. In addition, agents in complex tasks may have limitations, such as physical constraints or designer-imposed approximations of the task that make learning tractable. Limitations prevent agents from acting optimally, which complicates the already challenging problem. A learning agent must effectively compensate for its own limitations while exploiting the limitations of the other agents. My thesis research focuses on these two challenges, namely multiagent learning and limitations, and includes four main contributions.
Citation
M. Bowling. "Multiagent learning in the presence of agents with limitations". PhD Thesis, Carnegie Mellon University, May 2003.| Keywords: | machine learning, multiagent systems, game theory, reinforcement learning |
| Category: | PhD Thesis |
BibTeX
@phdthesis{Bowling:03,
author = {Michael Bowling},
title = {Multiagent learning in the presence of agents with limitations},
Type = {Carnegie Mellon University},
year = 2003,
}Last Updated: September 20, 2009Submitted by Dale Schuurmans
