Carlos Diuk Wasser Department of Computer Science Rutgers University

Contact Me:

• Email: cdiuk AT cs.rutgers.edu
• Office Phone:  732-445-6185
• Office:  Hill 427 (RL^3 lab)
• Mailing Address:
  Hill Center - Busch Campus
  110 Frelinghuysen Road
  Piscataway, New Jersey 08855

Research:

I'm working with Michael Littman at the Rutgers Laboratory for Real-Life Reinforcement Learning (RL)^3.

I'm currently involved in a couple of projects:

• Transfer learning
  • How can an agent "transfer" knowledge obtained while performing a previous task into a new and -somewhat- similar task?
• Efficient reinforcement learning
  • Exploring how factored state spaces, hierarchies, object-oriented representations and model-based frameworks can be combined to achieve efficient learning.

Publications:

2009. “The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning”, Carlos Diuk, Lihong Li and Bethany Leffler. ICML 2009. pdf / videolecture

2009. “Exploring Compact Reinforcement-Learning Representations with Linear Regression”, Thomas J. Walsh, István Szita, Carlos Diuk, and Michael L. Littman. UAI 2009. pdf

2008. “An Object-Oriented Representation for Efficient Reinforcement Learning”, Carlos Diuk, Andre Cohen and Michael L. Littman. ICML 2008. pdf / videolecture

2008. “Hierarchical Reinforcement Learning”, Carlos Diuk and Michael Littman. Encyclopedia of Artificial Intelligence, IGI Global, July 2008.

2007. “Efficient Structure Learning in Factored-state MDPs”, Alexander L. Strehl, Carlos Diuk and Michael L. Littman. AAAI 2007.pdf

2007. “An adaptive anomaly detector for worm detection ”, John Mark Agosta, Carlos Diuk, Jaideep Chandrashekar and Carl Livadas. Second Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (sysML-07). pdf

2006. “A Hierarchical Approach to Efficient Reinforcement Learning in Deterministic Domains”, Carlos Diuk, Alexander L. Strehl and Michael L. Littman. AAMAS’06. pdf

2003. “Una herramienta computacional para la reconstrucción de genealogías históricas.”, Carlos Diuk. Licenciatura Dissertation. Dept. of Computer Science, Universidad de Buenos Aires.pdf

2002. “Computer tools for reconstructing a genealogy”, Carlos Diuk and Enrique Tándeter. International Journal of History and Computing. Edinburgh University Press.

Workshop presentations:

2006. Invited Speaker at AAMAS Hierarchical Autonomous Agents and Multi-Agent Systems: “A Hierarchical Approach to Efficient Reinforcement Learning”.

2006. “Using Classifiers to Transfer Knowledge ”, Thomas J. Walsh, Carlos Diuk and Michael Littman. Presented at the New York Academy of Science Machine Learning Symposium.

2006. “Efficient exploration and learning of structure in factored-state MDPs ”, Carlos Diuk, Michael L. Littman, Alexander L. Strehl. Presented at NIPS Workshop “Towards a New Reinforcement Learning?”.

2005. “A Hierarchical Approach to Efficient Reinforcement Learning in Factored State Spaces”, Carlos Diuk, Michael L. Littman, and Alexander L. Strehl. Presented at the the 22nd International Conference on Machine Learning (ICML 2005), Workshop on Rich Representations for Reinforcement Learning, Bonn, Germany, 2005.

Teaching:

Download full CV

Links:

Friends

• Rohan Fernandes.
• Miguel Angel Mosteiro.