Matthew Hausknecht
Neural Networks Lab: Former Collaborator
Learning Agents Lab: Ph.D. Alumni
Matthew's research focuses on understanding the principles of cerebellar learning and its applications to pattern recognition, supervised learning, and control. Additionally, he investigates general purpose learning by developing general game playing agents for Atari 2600 video games. In his spare time Matthew is an avid rock climber.
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Deep Imitation Learning for Parameterized Action Spaces Matthew Hausknecht and Yilun Chen and Peter Stone In AAMAS Adaptive Learning Agents (ALA) Workshop, Singapore, May 2016. 2016

Deep Reinforcement Learning in Parameterized Action Space Matthew Hausknecht and Peter Stone In Proceedings of the International Conference on Learning Representations (ICLR), San Juan, ... 2016

Grounded Semantic Networks for Learning Shared Communication Protocols Matthew Hausknecht and Peter Stone In Deep Reinforcement Learning, NIPS Workshop, Barcelona, Spain, December 2016. 2016

Half Field Offense: An Environment for Multiagent Learning and Ad Hoc Teamwork Matthew Hausknecht and Prannoy Mupparaju and Sandeep Subramanian and Shivaram Kalyanakrishnan and Pe... In AAMAS Adaptive Learning Agents (ALA) Workshop, Singapore, May 2016. 2016

Machine Learning Capabilities of a Simulated Cerebellum Matthew Hausknecht and Wen-Ke Li and Michael Mauk and Peter Stone IEEE Transactions on Neural Networks and Learning Systems, January 2016. 2016

On-Policy vs. Off-Policy Updates for Deep Reinforcement Learning Matthew Hausknecht and Peter Stone In Deep Reinforcement Learning: Frontiers and Challenges, IJCAI Workshop, New York, July 2016... 2016

Deep Recurrent Q-Learning for Partially Observable MDPs Matthew Hausknecht and Peter Stone In AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents (AAAI-SDMIA15), A... 2015

The Impact of Determinism on Learning Atari 2600 Games Matthew Hausknecht and Peter Stone In AAAI Workshop on Learning for General Competency in Video Games, Austin, Texas, USA, Janua... 2015

A Neuroevolution Approach to General Atari Game Playing Matthew Hausknecht and Joel Lehman and Risto Miikkulainen and Peter Stone IEEE Transactions on Computational Intelligence and AI in Games, 2013. 2013

HyperNEAT-GGP: A HyperNEAT-based Atari General Game Player Matthew Hausknecht, Piyush Khandelwal, Risto Miikkulainen, Peter Stone In Genetic and Evolutionary Computation Conference (GECCO) 2012, 2012. 2012

Using a million cell simulation of the cerebellum: Network scaling and task generality Wen-Ke Li and Matthew J. Hausknecht and Peter Stone and Michael D. Mauk Neural Networks, November 2012. 2012

Austin Villa 2010 Standard Platform Team Report Samuel Barrett and Katie Genter and Matthew Hausknecht and Todd Hester and Piyush Khandelwal and Juh... Technical Report, Department of Computer Science, The University of Texas at Austin, January 2011. T... 2011

Autonomous Intersection Management: Multi-Intersection Optimization Matthew Hausknecht and Tsz-Chiu Au and Peter Stone In Proceedings of IROS 2011-IEEE/RSJ International Conference on Intelligent Robots and Systems (... 2011

Dynamic Lane Reversal in Traffic Management Matthew Hausknecht and Tsz-Chiu Au and Peter Stone and David Fajardo and Travis Waller In Proceedings of IEEE Intelligent Transportation Systems Conference (ITSC), October 2011. 2011

Learning Powerful Kicks on the Aibo ERS-7: The Quest for a Striker Matthew Hausknecht and Peter Stone In Robocup International Symposium, 2010. 2010

Vision Calibration and Processing on a Humanoid Soccer Robot Piyush Khandelwal and Matthew Hausknecht and Juhyun Lee and Aibo Tian and Peter Stone In The Fifth Workshop on Humanoid Soccer Robots at Humanoids 2010, Nashville, TN, 2010. 2010