research
∙
01/03/2023
Faster Approximate Dynamic Programming by Freezing Slow States
We consider infinite horizon Markov decision processes (MDPs) with fast-...
share
research
∙
11/12/2021
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Bayesian optimization (BO) is a sample-efficient approach to optimizing ...
research
∙
06/29/2020
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Bayesian optimization is a sequential decision making framework for opti...
research
∙
06/28/2020
Lookahead-Bounded Q-Learning
We introduce the lookahead-bounded Q-learning (LBQL) algorithm, a new, p...
research
∙
10/21/2019
Exploration via Sample-Efficient Subgoal Design
The problem of exploration in unknown environments continues to pose a c...
research
∙
10/14/2019
BoTorch: Programmable Bayesian Optimization in PyTorch
Bayesian optimization provides sample-efficient global optimization for ...
research
∙
05/15/2018
Feedback-Based Tree Search for Reinforcement Learning
Inspired by recent successes of Monte-Carlo tree search (MCTS) in a numb...
research
∙
04/20/2017
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds
Monte Carlo Tree Search (MCTS), most famously used in game-play artifici...
research
∙
09/07/2015
