6 papers
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…
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…
Taming the Adversary: Stable Minimax Deep Deterministic Policy Gradient via Fractional Objectives
Taeho Lee, Donghwan Lee
Reinforcement learning (RL) has achieved remarkable success in a wide range of control and decision-making tasks. However, RL agents often exhibit unstable or degraded performance…
Analysis of approximate linear programming solution to Markov decision problem with log barrier function
Donghwan Lee, Hyukjun Yang, Bum Geun Park
There are two primary approaches to solving Markov decision problems (MDPs): dynamic programming based on the Bellman equation and linear programming (LP). Dynamic programming meth…
Robust Deterministic Policy Gradient for Disturbance Attenuation and Its Application to Quadrotor Control
Taeho Lee, Donghwan Lee
This paper presents a robust reinforcement learning algorithm called robust deterministic policy gradient (RDPG), which reformulates the H-infinity control problem as a two-player…
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…