3 citations · 6 across the 8 of their papers we have counts for
5 papers · 1 filter
Finite-Time Bounds for Average-Reward Fitted Q-Iteration
Jongmin Lee, Ernest K. Ryu
Although there is an extensive body of work characterizing the sample complexity of discounted-return offline RL with function approximations, prior work on the average-reward sett…
Why Policy Gradient Algorithms Work for Undiscounted Total-Reward MDPs
Jongmin Lee, Ernest K. Ryu
The classical policy gradient method is the theoretical and conceptual foundation of modern policy-based reinforcement learning (RL) algorithms. Most rigorous analyses of such meth…
Deflated Dynamics Value Iteration
Jongmin Lee, Amin Rakhsha, Ernest K. Ryu +1
The Value Iteration (VI) algorithm is an iterative procedure to compute the value function of a Markov decision process, and is the basis of many reinforcement learning (RL) algori…
Accelerating Value Iteration with Anchoring
Jongmin Lee, Ernest K. Ryu
Value Iteration (VI) is foundational to the theory and practice of modern reinforcement learning, and it is known to converge at a -rate, where is the discoun…
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks
Jongmin Lee, Joo Young Choi, Ernest K. Ryu +1
The tremendous recent progress in analyzing the training dynamics of overparameterized neural networks has primarily focused on wide networks and therefore does not sufficiently ad…