560 citations · 3.3k across the 60 of their papers we have counts for
4 papers · 2 filters
Incremental Model-based Learners With Formal Learning-Time Guarantees
Alexander L. Strehl, Lihong Li, Michael L. Littman
Model-based learning algorithms have been shown to use experience efficiently when learning to solve Markov Decision Processes (MDPs) with finite state and action spaces. However,…
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Emma Brunskill, Bethany Leffler, Lihong Li +2
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinforc…
A Bayesian Sampling Approach to Exploration in Reinforcement Learning
John Asmuth, Lihong Li, Michael L. Littman +2
We present a modular approach to reinforcement learning that uses a Bayesian representation of the uncertainty over models. The approach, BOSS (Best of Sampled Set), drives explora…
Exploring compact reinforcement-learning representations with linear regression
Thomas J. Walsh, Istvan Szita, Carlos Diuk +1
This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm imp…