Matthew E. Taylor
509-335-6457 (but I prefer email)
Allred Distinguished Professorship in Artificial Intelligence
School of Electrical Engineering and Computer Science
Washington State University
Pullman, WA 99164
Director of the IRL Lab
Matt was elected to serve as one of the 27 members of the IFAAMAS Board of directors.
We're very fortunate to have won two grants from the Air Force Research Laboratory:
Recent paper acceptances:
- Lifelong Transfer Learning for Heterogenous Teams of Agents in Sequential Decision Processes with Eric Eaton (Co-PI) and Paul Ruvolo (Co-PI)
- Curriculum Development for Transfer Learning in Dynamic Multiagent Settings with Peter Stone (PI)
Agents Teaching Agents in Reinforcement Learning (Nectar Abstract) by Matthew E. Taylor and Lisa Torrey
Learning Something from Nothing: Leveraging Implicit Human Feedback Strategies by
Robert Loftin, B. Peng, J. MacGlashan, M. Littman, M. E. Taylor, D. Roberts, and J. Huang
- IAT-14: CLEANing the Reward: Counterfactual Actions Remove Exploratory Action Noise in Multiagent Learning by Chris HolmesParker, Mathew Talor, Adrian Agogino, and Kagan Tumer
- AAAI-14: Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence by Tim Brys, A.Nowe, D. Kudenko, and M. E. Taylor
- AAAI-14: A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback by Robert Loftin, J. MacGlashan, M. Littman, M. E. Taylor, D. Roberts, and J. Huang
AAAI-14 MLIS workshop:
Training an Agent to Ground Commands with Reward and Punishment by James Macglashan, M. Littman, R. Loftin, B. Peng, D. Roberts and M. E. Taylor
AAAI-14 MLIS workshop:
An Automated Measure of MDP Similarity for Transfer in Reinforcement Learning by Haitham Bou Ammar, E. Eaton, M. E. Taylor, D. C. Mocanu, K. Driessens, G. Weiss, and K. Tuyls
Multi-Objectivization of Reinforcement Learning Problems by Reward Shaping by Tim Brys, A. Harutyunyan, P. Vrancx, M. E. Taylor, D. Kudenko, and A. Nowe.
Online Multi-Task Learning for Policy Gradient Methods by Haitham Bou Ammar, P. Ruvolo, M. E. Taylor, and E. Eaton
- Journal of Connection Science:
Reinforcement Learning Agents Providing Advice in Complex Video Games by Matthew E. Taylor, Nicholas Carboni, Anestis Fachantidis, Ioannis Vlahavas and Lisa Torrey.
I have worked with Milind Tambe as part of the
TEAMCORE research group and am also a
former member of
Agents Research Group, directed
by Peter Stone.
My research focuses on agents, physical or virtual entities
that interact with their environments. My main goals are to enable
individual agents, and teams of agents, to
Additionally, I am interested in exploring how agents can learn from
humans, whether the human is explicitly teaching the agent, the agent
is passively observing the human, or the agent is actively cooperating
with the human on a task.
- learn tasks in real world environments that are not fully known when the agents are designed;
- perform multiple tasks, rather than just a single task; and
- allow agents to robustly coordinate with, and reason about, other agents.
A selection of current and past research projects follows.
Spring 2014: CptS 580-03: Intelligent Agents
Fall 2013: CptS 483: Introduction to Robotics
Previous courses: here
View my CV as: pdf
Matthew E. Taylor
graduated magna cum laude with a double major in computer
science and physics from Amherst College in 2001. After working for
two years as a software developer, he began his Ph.D. work at the University of Texas at Austin with an MCD
fellowship from the College of Natural Sciences. He received his
doctorate from the Department of Computer Sciences in the summer of 2008, supervised by Peter Stone.
Matt then completed a two year
postdoctoral research position at the
University of Southern California with Milind Tambe and spent 2.5 years as an assistant professor at Lafayette College in the computer science department. He is currently an assistant professor at Washington State University in the School of Electrical Engineering and Computer Science and is a recipient of the National Science Foundation CAREER award.
Current research interests
include intelligent agents, multi-agent systems, reinforcement learning, transfer learning, and robotics.