4 papers · 1 filter
Explore Beyond the Boundary Using Entropic Information
Bumgeun Park, Donghwan Lee
In reinforcement learning, exploration with sparse and delayed rewards presents a significant challenge due to the limited feedback available for guiding the learning process. Addr…
Taming the Adversary: A Cost-to-Disturbance Ratio Approach to Adversarial Reinforcement Learning
Taeho Lee, Donghwan Lee
Reinforcement learning (RL) policies trained in simulation often degrade once deployed on real systems, where the controller must reject external disturbances that were never encou…
Bellman Residual Minimization for Control: Geometry, Stationarity, and Convergence
Donghwan Lee, Hyukjun Yang
Markov decision problems are most commonly solved via dynamic programming. Another approach is Bellman residual minimization, which directly minimizes the squared Bellman residual…
Deep Q-Learning with Gradient Target Tracking
Bum Geun Park, Taeho Lee, Donghwan Lee
This paper introduces Q-learning with gradient target tracking, a novel reinforcement learning framework that provides a learned continuous target update mechanism as an alternativ…