I graduated from the University of Alberta with a Ph.D. in Computing Science in 2015. During my doctoral studies I was advised by Richard Sutton, working in the RLAI lab. After that I worked as a Post-doc and Research Scientist in the Department of Computer Science at Indiana University in Bloomington.
If you are interested in joining my group as an MSc student, please apply directly to the MSc program. Do not contact me! I have no control over the admissions process: admission is based on grades, previous research experience, your research statement, and the quality of your reference letters. All students accepted to our MSc program get guaranteed TA funding. If you would like to work with me, then first apply to the MSc program, then contact once your are admitted.
Sina Ghiassian (PhD)
Raksha Kumaraswamy (PhD)
Derek Li (MSc)
Banafshe Rafiee (PhD)
Eugene Chen (MSc)
Andrew Jacobsen (PhD)
Cam Linke (MSc)
Matthew McLeod (MSc)
Archit Sakhadeo (MSc)
Han Wang (PhD)
Xutong Zhao (MSc)
CMPUT 607: Empirical Reinforcement Learning - Winter 2021
CMPUT 397: Reinforcement Learning I - Fall 2019
CMPUT 366: Intelligent Systems - Fall 2018
CMPUT 366: Intelligent Systems - Fall 2017
CMPUT 609: Reinforcement Learning - Fall 2017
CSCI-B 659: Reinforcement learning for Artificial Intelligence - Spring 2017
CSCI-B 659: Reinforcement learning for Artificial Intelligence - Spring 2016
Reinforcement Learning, Robotics, Knowledge Representation and
My research focuses on the problem of Artificial Intelligence, specifically how to replicate or simulate human-level intelligence in physical and simulated agents. My research program explores how the problem of intelligence can be modelled as a reinforcement learning agent interacting with some unknown environment, learning from a scalar reward signal rather than explicit feedback. My contributions include new algorithms for reinforcement learning, and large-scale demonstrations of learning on mobile robots.
My current CV can be found
Linke, C., Ady, N. M., White, M., Degris, T., & White, A. (2020). Adapting behaviour via intrinsic reward: A survey and empirical study. Journal of Artificial Intelligence Research.
Schlegel, M., Jacobsen, A., Zaheer, M., Patterson, A., White, A., & White, M. (2020). General value function networks. Journal of Artificial Intelligence Research.
Modayil, J., White, A., Sutton, R. S. (2014). Multi-timescale Nexting
in a Reinforcement Learning Robot. Adaptive Behavior, 22(2):146--160.
Whiteson, S., Tanner, B., & White, A. (2010). The reinforcement
learning competitions. AI Magazine, 31(2): 81--94.
Tanner, B., & White, A. (2009). RL-Glue: Language-independent software for reinforcement-learning experiments. The Journal of Machine Learning Research, 10: 2133--2136.
Ghiassian S., Patterson A., Garg S., Gupta D., White A., White M.(2020). Gradient Temporal-Difference Learning with Regularized Corrections. International Conference on Machine Learning (ICML).
Ghiassian S., Rafiee B., Long Lo Y., White A. (2020). Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).
Nath S, Liu V., Chan A., White A., White M. (2020). Training Recurrent Neural Networks Online by Learning Explicit State Variables. International Conference on Learning Representations (ICLR).
Wan Y., Zaheer M., Sutton R., White A., White M. (2019). Planning with Expectation Models. The International Joint Conference on
Artificial Intelligence (IJCAI).
Rafiee B., Ghiassian S., White, A., Sutton R. (2019). Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots
. The 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
Jacobsen A., Schlegel M., Linke C., Degris T., White, A., White M. (2019). Meta-descent for online, continual prediction
. AAAI Conference on Artificial Intelligence.
Kumaraswamy R., Schlegel M., White, A., White M. (2018). Context-dependent upper-confidence bounds for directed exploration
. Advances in Neural Information Processing Systems (NIPS).
Sherstan C., Bennett B., Young K., Ashley D., White, A., White M., Sutton R. (2018). Directly Estimating the Variance of the $\lambda$-Return Using Temporal-Difference Methods
. Conference on Uncertainty in Artificial Intelligence (UAI).
Pan Y., Zaheer M., White, A., Patterson A., White M. (2018). Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains
. International Joint Conference on Artificial Intelligence (IJCAI).
Pan Y., White, A., White M. (2017). Accelerated Gradient Temporal Difference Learning
. AAAI Conference on Artificial Intelligence (AAAI).
Sherstan, C., Machado, M., ,White, A., Patrick P. (2016). Introspective Agents: Confidence Measures for General Value
Functions, Artificial General Intelligence (AGI).
White, A., White M. (2016). Investigating practical linear temporal difference learning. In International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). [ CODE ]
White, M., White A. (2016) Adapting the trace parameter in reinforcement learning, In International Conference on Autonomous Agents and MultiAgent Systems (AAMAS).
White, A., Modayil, J., & Sutton, R. S. (2012). Scaling
life-long off-policy learning. In the IEEE International Conference on Development and Learning and
Epigenetic Robotics, 1--6.
[paper of distinction award]
Modayil, J., White, A., Pilarski, P. M., & Sutton, R. S. (2012). Acquiring a broad
range of empirical knowledge in real time by temporal-difference
learning. In the IEEE International Conference on Systems,
Man, and Cybernetics, 1903--1910.
Modayil, J., White, A., Sutton, R. S. (2012). Multi-timescale Nexting
in a Reinforcement Learning Robot. Presented at the 2012 International
Conference on Adaptive Behaviour, Odense, Denmark. To appear in: SAB
12, LNAI 7426, pp. 299-309, T. Ziemke, C. Balkenius, and J. Hallam,
Eds., Springer Heidelberg.
Sutton, R. S., Modayil, J., Delp, M., Degris, T., Pilarski, P. M.,
White, A., & Precup, D. (2011). Horde: A
scalable real-time architecture for learning knowledge from
unsupervised sensorimotor interaction. In The 10th
International Conference on Autonomous Agents and Multiagent
Systems: 2, 761--768.
White, M., & White, A. (2010). Interval
estimation for reinforcement-learning algorithms in continuous-state
domains. In Advances in Neural Information Processing Systems, 2433--2441.
Sturtevant, N. R., & White, A. M. (2007). Feature
construction for reinforcement learning in hearts. In
Computers and Games . Springer Berlin Heidelberg, 122--134
Linke C., Ady N., White M., Degris T., White A. (2020) Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study.
Schlegel M., Jacobsen A., Zaheer M., Patterson A., White A., White M. (2020) General Value Function Networks.
Ghiassian S., Patterson A., White M.,, Sutton R. S., White A. (2019) Online Off-policy Prediction.
Other published works
Yasui N., Lim S., Linke C., White A., White M. (2019). An Empirical and Conceptual Categorization of
Value-based Exploration Methods. ICML Exploration in Reinforcement Learning
Pan Y., White, A., White M. (2017). Accelerated Gradient Temporal Difference Learning
. European workshop on reinforcement learning (EWRL).
Schlegel M., White, A., White M. (2017). Stable predictive representations with general value functions for continual learning
. Continual Learning and Deep Networks workshop at the Neural Information Processing System Conference.
White, A., & Sutton, R. S. (2014). GQ (lambda) Quick Reference Guide.
White, A., Modayil, J., & Sutton, R. S. (2014). Surprise and
curiosity for big data robotics. In Workshops at the
Twenty-Eighth AAAI Conference on Artificial Intelligence.
Modayil, J., White, A., Pilarski, P. M., Sutton, R. S. (2012). Acquiring
Diverse Predictive Knowledge in Real Time by Temporal-difference
Learning. International Workshop on Evolutionary and
Reinforcement Learning for Autonomous Robot Systems, Montpellier, France.
[Best paper award]
Modayil, J., Pilarski, P., White, A., Degris, T., & Sutton,
R. (2010). Off-policy knowledge maintenance for robots. In Proceedings
of Robotics Science and Systems Workshop (Towards Closing the Loop:
Active Learning for Robotics) : 55.
White, A. (2015) Developing a predictive approach to knowledge. Doctoral thesis, University of Alberta.
White, A. (2006) A standard system for benchmarking in reinforcement
learning. Master's thesis, University of Alberta.
google scholar page for a list of my
publications that Google knows about.
Office: 307 Athabasca Hall
Department of Computing Science
University of Alberta