200 citations · 208 across the 2 of their papers we have counts for
2 papers
cs.LG2026★ 200 cited
An Analysis of Reinforcement Learning with Function Approximation
Francisco S. Melo, Sean P. Meyn, M. Isabel Ribeiro
We address the problem of computing the optimal Q-function in Markov decision problems with infinite state-space. We analyze the convergence properties of several variations of Q-l…
cs.LG2019★ 8 cited
Zap Q-Learning With Nonlinear Function Approximation
Shuhang Chen, Adithya M. Devraj, Fan Lu +2
Zap Q-learning is a recent class of reinforcement learning algorithms, motivated primarily as a means to accelerate convergence. Stability theory has been absent outside of two res…