4 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…
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…
Adaptive Policy Backbone via Shared Network
Bumgeun Park, Donghwan Lee
Reinforcement learning (RL) has achieved impressive results across domains, yet learning an optimal policy typically requires extensive interaction data, limiting practical deploym…
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…