Matthew Taylor taylorm@lafayette.edu Assistant Professor Department of Computer Science Lafayette College Easton, PA 18042 Office: Acopian Engineering Center, room 522 610-330-5417

• Reinforcement learning transfer via sparse coding. Haitham Bou Ammar, Karl Tuyls, Matthew E. Taylor, Kurt Driessen, and Gerhard Weiss. (Full Paper)
• Towards student/teacher learning in sequential decision tasks. Lisa Torrey and Matthew E. Taylor. (Extended Abstract)

• An Empirical Analysis of RL's Drift From Its Behaviorism Roots. Matthew Adams, Robert Loftin, Matthew E. Taylor, Michael Littman and David Roberts. (Long Oral Presentation)
• Help an Agent Out: Student/Teacher Learning in Sequential Decision Tasks. Lisa Torrey and Matthew E. Taylor. (Long Oral Presentation)

Research

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.

		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
		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

Code

• Model Assignment: Reinforcement Learning in a Generalized Mario Domain (EAAI-11)
• Matlab code for simple metric learning in reinforcement learning is available. (AAMAS-11)
• Selected code from my dissertation can be found in the on-line appendix.

• My Google Scholar page, with citation statistics, is here.
• I was part of a one day tutorial on reinforcement learning at AAMAS-10.
• I gave a tutorial with Alessandro Lazaric on transfer learning in RL at AAMAS-09.
• I was a co-chair of the ALA workshop at AAMAS-09.
• I helped organize the 2009 RL Competition.
• I organized an AAAI-08 workshop: Transfer Learning for Complex Tasks.