38 citations · 59 across the 3 of their papers we have counts for
7 papers
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Beining Han, Chongyi Zheng, Harris Chan +3
Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes aft…
Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels
Harris Chan, Jamie Kiros, William Chan
A channel corresponds to a viewpoint or transformation of an underlying meaning. A pair of parallel sentences in English and French express the same underlying meaning, but through…
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis, Harris Chan, Stephen Zhao +2
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a u…
An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
Silviu Pitis, Harris Chan, Kiarash Jamali +1
Distances are pervasive in machine learning. They serve as similarity measures, loss functions, and learning targets; it is said that a good distance measure solves a task. When de…
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning
Harris Chan, Yuhuai Wu, Jamie Kiros +2
Sparse reward is one of the most challenging problems in reinforcement learning (RL). Hindsight Experience Replay (HER) attempts to address this issue by converting a failed experi…
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise
Yeming Wen, Kevin Luk, Maxime Gazeau +3
The choice of batch-size in a stochastic optimization algorithm plays a substantial role for both optimization and generalization. Increasing the batch-size used typically improves…