93 citations · 225 across the 20 of their papers we have counts for
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Finite-Time Analysis of Temporal Difference Learning with Experience Replay
Han-Dong Lim, Donghwan Lee
Temporal-difference (TD) learning is widely regarded as one of the most popular algorithms in reinforcement learning (RL). Despite its widespread use, it has only been recently tha…
Backstepping Temporal Difference Learning
Han-Dong Lim, Donghwan Lee
Off-policy learning ability is an important feature of reinforcement learning (RL) for practical applications. However, even one of the most elementary RL algorithms, temporal-diff…
Finite-Time Accuracy of Temporal-Difference Learning Under Schur-Stable Recursions
Donghwan Lee, Do Wan Kim
Temporal difference (TD) learning is a cornerstone reinforcement learning (RL) method for policy evaluation, where the goal is to estimate the value function of a Markov decision p…
New Versions of Gradient Temporal Difference Learning
Donghwan Lee, Han-Dong Lim, Jihoon Park +1
Sutton, Szepesvári and Maei introduced the first gradient temporal-difference (GTD) learning algorithms compatible with both linear function approximation and off-policy training.…
Periodic Q-Learning
Donghwan Lee, Niao He
The use of target networks is a common practice in deep reinforcement learning for stabilizing the training; however, theoretical understanding of this technique is still limited.…
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
Donghwan Lee, Niao He, Parameswaran Kamalaruban +1
This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and coo…