15 citations · 69 across the 19 of their papers we have counts for
4 papers · 1 filter
Safe Reinforcement Learning Using Advantage-Based Intervention
Nolan Wagener, Byron Boots, Ching-An Cheng
Many sequential decision problems involve finding a policy that maximizes total reward while obeying safety constraints. Although much recent research has focused on the developmen…
Bellman-consistent Pessimism for Offline Reinforcement Learning
Tengyang Xie, Ching-An Cheng, Nan Jiang +2
The use of pessimism, when reasoning about datasets lacking exhaustive exploration has recently gained prominence in offline reinforcement learning. Despite the robustness it adds…
Heuristic-Guided Reinforcement Learning
Ching-An Cheng, Andrey Kolobov, Adith Swaminathan
We provide a framework for accelerating reinforcement learning (RL) algorithms by heuristics constructed from domain knowledge or offline data. Tabula rasa RL algorithms require en…
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation
Andrea Zanette, Ching-An Cheng, Alekh Agarwal
Policy optimization methods are popular reinforcement learning algorithms, because their incremental and on-policy nature makes them more stable than the value-based counterparts.…