31 citations · 72 across the 22 of their papers we have counts for
3 papers · 1 filter
Randomized Value Functions via Posterior State-Abstraction Sampling
Dilip Arumugam, Benjamin Van Roy
State abstraction has been an essential tool for dramatically improving the sample efficiency of reinforcement-learning algorithms. Indeed, by exposing and accentuating various typ…
Reparameterized Variational Divergence Minimization for Stable Imitation
Dilip Arumugam, Debadeepta Dey, Alekh Agarwal +3
While recent state-of-the-art results for adversarial imitation-learning algorithms are encouraging, recent works exploring the imitation learning from observation (ILO) setting, w…
Flexible and Efficient Long-Range Planning Through Curious Exploration
Aidan Curtis, Minjian Xin, Dilip Arumugam +2
Identifying algorithms that flexibly and efficiently discover temporally-extended multi-phase plans is an essential step for the advancement of robotics and model-based reinforceme…