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About Me

I am a Senior Researcher in the Reinforcement Learning Group at Microsoft Research in Redmond, WA. I previously obtained a PhD in Computer Science from the University of Texas at Austin advised by Professor Peter Stone.

I work at the intersection of deep learning and reinforcement learning to develop autonomous agents capable of adapting and learning in complex environments.

Open Source Software

I’ve built or contributed significantly to the following open-source projects:

Jericho is a lightweight python-based interface connecting learning agents with interactive fiction games. It serves as a testbed for benchmarking progress of language-based agents on man-made text games. BlogPost
Half Field Offense (HFO) is a multiagent subtask in RoboCup simulated soccer, modeling a situation in which the offense of one team has to get past the defense of the opposition in order to shoot goals.
Arcade Learning Environment (ALE) Is a well known platform for training learning agents to play Atari games. I developed the first external interface to ALE which allowed the environment to be used as a library.

Publications

Keep CALM and Explore: Language Models for Action Generation in Text-based Games
S Yao, R Rao, M Hausknecht, K Narasimhan. Empirical Methods in Natural Language Processing (EMNLP) 2020
[Code]
Working Memory Graphs
R Loynd, R Fernandez, A Celikyilmaz, A Swaminathan, M Hausknecht. International Conference on Machine Learning (ICML) 2020
[Code]
Learning Calibratable Policies using Programmatic Style-Consistency
E Zhan, A Tseng, Y Yue, A Swaminathan, M Hausknecht. International Conference on Machine Learning (ICML) 2020
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
P Ammanabrolu, M Hausknecht. International Conference on Learning Representations (ICLR) 2020
[Code]
Interactive Fiction Games: A Colossal Adventure
M Hausknecht, P Ammanabrolu, M Cote, X Yuan. Association for the Advancement of Artificial Intelligence (AAAI) 2020
[Code][Blog Post]
Scriptnet: Neural static analysis for malicious javascript detection
J Stokes, R Agrawal, G McDonald, M Hausknecht.IEEE Military Communications Conference (MILCOM) 2019
Nail: A general interactive fiction agent
M Hausknecht, R Loynd, G Yang, A Swaminathan, JD Williams.Technical Report 2019
[Code]
Counting to Explore and Generalize in Text-based Games
X Yuan, M Cote, A Sordoni, R Laroche, R Tachet des Combes, M Hausknecht, A Trischler. European Workshop on Reinforcement Learning (EWRL) 2018
TextWorld: A Learning Environment for Text-based Games
M Cote, A Kadar, X Yuan, B Kybartas, T Barnes, E Fine, J Moore, M Hausknecht, L Asri, M Adada, W Tay, A Trischler. IJCAI/ICML Computer Games Workshop 2018
[Code]
Leveraging grammar and reinforcement learning for neural program synthesis
R Bunel, M Hausknecht, J Devlin, R Singh, P Kohli. International Conference on Learning Representations (ICLR) 2018
Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
MC Machado, MG Bellemare, E Talvitie, J Veness, M Hausknecht, M Bowling. International Joint Conferences on Artificial Intelligence (IJCAI) 2017
Neural Program Meta-Induction
J Devlin, RR Bunel, R Singh, M Hausknecht, P Kohli.Advances in Neural Information Processing Systems (NeurIPS) 2017
Cooperation and communication in multiagent deep reinforcement learning
Matthew Hausknecht. PhD Thesis 2016
Half field offense: An environment for multiagent learning and ad hoc teamwork
M Hausknecht, P Mupparaju, S Subramanian, S Kalyanakrishnan, P Stone. AAMAS Adaptive Learning Agents (ALA) Workshop 2016
On-policy vs. off-policy updates for deep reinforcement learning
M Hausknecht, P Stone.Deep Reinforcement Learning: Frontiers and Challenges, (IJCAI) Workshop 2016
Deep Reinforcement Learning in Parameterized Action Space
M Hausknecht, P Stone. Proceedings of the International Conference on Learning Representations (ICLR) 2016
[Code]
Machine Learning Capabilities of a Simulated Cerebellum
M Hausknecht, W Li, M Mauk, and P Stone. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2016
Deep Recurrent Q-Learning for Partially Observable MDPs
M Hausknecht, P Stone. AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents 2015
Beyond Short Snippets: Deep Networks for Video Classification
J Ng, M Hausknecht, S Vijayanarasimhan, O Vinyals, R Monga, G Toderici. Conference on Computer Vision and Pattern Recognition (CVPR) 2015
A Neuroevolution Approach to General Atari Game Playing
M Hausknecht, J Lehman, R Miikkulainen, and P Stone. IEEE Transactions on Computational Intelligence and AI in Games (TCIAIG) 2014
Using a million cell simulation of the cerebellum: Network scaling and task generality
W Li, M Hausknecht, P Stone, and M Mauk. Neural Networks 2012
HyperNEAT-GGP: A HyperNEAT-based Atari General Game Player
M Hausknecht, P Khandelwal, R Miikkulainen, and P Stone. Proceedings of Genetic and Evolutionary Computation Conference (GECCO) 2012
Dynamic Lane Reversal in Traffic Management
M Hausknecht, T Au, P Stone, D Fajardo, and T Waller. Proceedings of IEEE Intelligent Transportation Systems Conference (ITSC) 2011
Autonomous Intersection Management: Multi-Intersection Optimization
M Hausknecht, T Au, and P Stone. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2011
Learning Powerful Kicks on the Aibo ERS-7: The Quest for a Striker
M Hausknecht, P Stone. Proceedings of the RoboCup International Symposium 2010
For want of a nail: How absences cause events
P Wolff, A Barbey, M Hausknecht. Journal of Experimental Psychology: General 2009