42 citations · 43 across the 4 of their papers we have counts for
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cs.LG2024
Confident Natural Policy Gradient for Local Planning in -realizable Constrained MDPs
Tian Tian, Lin F. Yang, Csaba Szepesvári
The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives while maximizing c…
cs.LG2022★ 1 cited
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions
Tian Tian, Kenny Young, Richard S. Sutton
Value iteration (VI) is a foundational dynamic programming method, important for learning and planning in optimal control and reinforcement learning. VI proceeds in batches, where…
cs.LG2019★ 42 cited
MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments
Kenny Young, Tian Tian
The Arcade Learning Environment (ALE) is a popular platform for evaluating reinforcement learning agents. Much of the appeal comes from the fact that Atari games demonstrate aspect…