4 papers · 1 filter
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…
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion
Hwanwoo Kim, Panos Toulis, Eric Laber
Temporal difference (TD) learning is a foundational algorithm in reinforcement learning (RL). For nearly forty years, TD learning has served as a workhorse for applied RL as well a…
Exploiting Concavity Information in Gaussian Process Contextual Bandit Optimization
Kevin Li, Eric Laber
The contextual bandit models sequential decision-making problems in which rewards depend on both the chosen action and observed context. In many domains such as medicine, business,…
Empirical Bound Information-Directed Sampling for Norm-Agnostic Bandits
Piotr M. Suder, Eric Laber
Information-directed sampling (IDS) is a powerful framework for solving bandit problems which has shown strong results in both Bayesian and frequentist settings. However, frequenti…