139 citations · 170 across the 18 of their papers we have counts for
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cs.LG2023★ 6 cited
Reinforcement Learning with Function Approximation: From Linear to Nonlinear
Jihao Long, Jiequn Han
Function approximation has been an indispensable component in modern reinforcement learning algorithms designed to tackle problems with large state spaces in high dimensions. This…
cs.LG2021★ 1 cited
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space
Jihao Long, Jiequn Han
Most existing theoretical analysis of reinforcement learning (RL) is limited to the tabular setting or linear models due to the difficulty in dealing with function approximation in…
cs.LG2021★ 2 cited
An Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation
Jihao Long, Jiequn Han, Weinan E
Reinforcement learning (RL) algorithms based on high-dimensional function approximation have achieved tremendous empirical success in large-scale problems with an enormous number o…