4 papers
Implicit Q-Learning and SARSA: Liberating Policy Control from Step-Size Calibration
Hwanwoo Kim, Eric Laber
Q-learning and SARSA are foundational reinforcement learning algorithms whose practical success depends critically on step-size calibration. Step-sizes that are too large can cause…
Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber
Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD() is highly sensitive to the choice of step-size and thu…
Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector design
Hwanwoo Kim, Simon Mak, Ann-Kathrin Schuetz +1
With advances in scientific computing, computer experiments are increasingly used for optimizing complex systems. However, for modern applications, e.g., the optimization of nuclea…
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
Hwanwoo Kim, Chong Liu, Yuxin Chen
Bayesian optimization (BO) is a widely used iterative algorithm for optimizing black-box functions. Each iteration requires maximizing an acquisition function, such as the upper co…