50 citations · 125 across the 14 of their papers we have counts for
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cs.AI2020★ 29 cited
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks
Yuqian Jiang, Sudarshanan Bharadwaj, Bo Wu +3
In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an o…
cs.AI2019★ 12 cited
Joint Inference of Reward Machines and Policies for Reinforcement Learning
Zhe Xu, Ivan Gavran, Yousef Ahmad +4
Incorporating high-level knowledge is an effective way to expedite reinforcement learning (RL), especially for complex tasks with sparse rewards. We investigate an RL problem where…