- Matthew E. Taylor's webpage
Matthew E. Taylor

EME 137
509-335-6457 (but I prefer email)

Assistant Professor
Allred Distinguished Professorship in Artificial Intelligence
School of Electrical Engineering and Computer Science
Washington State University
Pullman, WA 99164

Publications       Research       Teaching       CV       Code       Links       Bio


Matt was elected to serve as one of the 27 members of the IFAAMAS Board of directors.
We had a paper accepted at ICML-14: Online Multi-Task Learning for Policy Gradient Methods by Haitham Bou Ammar, Paul Ruvolo, Matthew E. Taylor, and Eric Eaton
We a grateful to have received a pair of grants from the Air Force Research Labs:
  • 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)

We have two papers at AAAI-14:
  • Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence by Tim Brys, Ann Nowe, Daniel Kudenko, and Matthew E. Taylor
  • A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback by Robert Loftin, James MacGlashan, Michael Littman, Matthew E. Taylor, David Roberts, and Jeff Huang

We have two (full) papers at the Adaptive and Learning Agents (ALA) workshop at AAMAS-14:
  • Agents Teaching Humans in Reinforcement Learning Tasks by Yusen Zhan, Anestis Fachantidis, Ioannis Vlahavas and Matthew E. Taylor. Note: This is Yusen's first 1st authored paper with our group!
  • Exploiting Structure and Agent-Centric Rewards to Promote Coordination in Large Multiagent Systems by Chris HolmesParker, Mathew E. Taylor, Yusen Zhan and Kagan Tumer

Our paper has been accetped to the IEEE International Joint Conference on Neural Networks (IJCNN-14): Multi-Objectivization of Reinforcement Learning Problems by Reward Shaping by Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko and Ann Nowe.
Our paper has been accepted to the 8th Hellenic Conference on Artificial Intelligence (SETN 2014): An Autonomous Transfer Learning Algorithm for TD-Learners by Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis Vlahavas.
Our work has been has been accepted to the journal Connection Science:


I have worked with Milind Tambe as part of the TEAMCORE research group and am also a former member of the Learning 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

  1. learn tasks in real world environments that are not fully known when the agents are designed;
  2. perform multiple tasks, rather than just a single task; and
  3. allow agents to robustly coordinate with, and reason about, other agents.
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.

A selection of current and past research projects follows.

Transfer Learning  

Transfer Learning

My dissertation focused on leveraging knowledge from a previous task to speed up learning in a novel task, focusing on reinforcement learning domains.
I gave a talk at AGI-08 that gives a brief introduction to, and motivation for, transfer learning.

Representative Publication:
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning (JMLR-07)
Full list of relevant publications
RL Agent  

Reinforcement Learning

Much of my graduate work centered on reinforcement learning (RL) tasks, where agents learn to perform (initially) unknown tasks by optimizing a scalar reward. RL is well suited to allowing both virtual and physical agents to learn when humans are unable (or unwilling) to design optimal solutions themselves.

Representative Publication
Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement Learning (JAAMAS-09)
Full list of relevant publications

Multi-agent Exploration and Optimization

Since coming to USC, one of the most exciting projects we have worked on is a version of Distributed Constraint Optimization Problem (DCOP) where the agents have unknown rewards. This may also be thought of as a multi-agent, multi-armed bandit. This problem is relevant for tasks that require coordination under uncertainty, such as in wireless sensor networks.

Representative Publication
DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks (IJCAI-09)
Full list of relevant publications


Spring 2014:
CptS 580-03: Intelligent Agents
Fall 2013: CptS 483: Introduction to Robotics
Previous courses: here  


View my CV as:



Brief Biography

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.