69 citations · 462 across the 66 of their papers we have counts for
5 papers · 2 filters
A Function Approximation Method for Model-based High-Dimensional Inverse Reinforcement Learning
Kun Li, Joel W. Burdick
This works handles the inverse reinforcement learning problem in high-dimensional state spaces, which relies on an efficient solution of model-based high-dimensional reinforcement…
Inverse Reinforcement Learning in Large State Spaces via Function Approximation
Kun Li, Joel W. Burdick
This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces. To avoid solving the computationally expensive reinforcement…
Bellman Gradient Iteration for Inverse Reinforcement Learning
Kun Li, Yanan Sui, Joel W. Burdick
This paper develops an inverse reinforcement learning algorithm aimed at recovering a reward function from the observed actions of an agent. We introduce a strategy to flexibly han…
Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces
Yanan Sui, Yisong Yue, Joel W. Burdick
We consider sequential decision making under uncertainty, where the goal is to optimize over a large decision space using noisy comparative feedback. This problem can be formulated…
Multi-dueling Bandits with Dependent Arms
Yanan Sui, Vincent Zhuang, Joel W. Burdick +1
The dueling bandits problem is an online learning framework for learning from pairwise preference feedback, and is particularly well-suited for modeling settings that elicit subjec…