7 citations · 23 across the 6 of their papers we have counts for
6 papers
Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis
Kun Li, Joel W. Burdick
This work handles the inverse reinforcement learning (IRL) problem where only a small number of demonstrations are available from a demonstrator for each high-dimensional task, ins…
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…
Online Inverse Reinforcement Learning via Bellman Gradient Iteration
Kun Li, Joel W. Burdick
This paper develops an online inverse reinforcement learning algorithm aimed at efficiently recovering a reward function from ongoing observations of an agent's actions. To reduce…
Clinical Patient Tracking in the Presence of Transient and Permanent Occlusions via Geodesic Feature
Kun Li, Joel W. Burdick
This paper develops a method to use RGB-D cameras to track the motions of a human spinal cord injury patient undergoing spinal stimulation and physical rehabilitation. Because clin…
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…