Safety-Constrained Policy Transfer with Successor Features
Transfer source policies to a target reinforcement learning task with safety constraints using Successor Features.
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Observed Adversaries in Deep Reinforcement Learning
We examine the problem of observed adversaries for deep policies, where observations of other agents can hamper robot performance.
IEEE T-AFFC Best Paper Award!
Our joint work with Desmond Ong, Jamil Zaki and Noah Goodman on Applying Probabilistic Programming to Affective Computing is one of 5 Best Papers (out of 82 ...
Fairness meets CMDPs
This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on CMDPs.
Kaiqi wins the 2022 Research Achievement Award.
Kaiqi Chen is awarded the Research Achievement Award for his RSS 2022 paper on Differentiable Social Projection for Human Robot Communication.
The Dynamics of Q-learning in Population Games
We develop an accurate physics-inspired model for describing how a population of Q-learning agents adapt as they interact.
Harold wins NUS ATEA 2022.
Harold has been awarded the University Annual Teaching Excellence Award (2022). Harold also won the Faculty Teaching Excellence Award and is now on Faculty H...