7 citations · 27 across the 8 of their papers we have counts for
8 papers
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach
Man Luo, Bowen Du, Wenzhe Zhang +5
The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from…
Channel Knowledge Map for Environment-Aware Communications: EM Algorithm for Map Construction
Kun Li, Peiming Li, Yong Zeng +1
Channel knowledge map (CKM) is an emerging technique to enable environment-aware wireless communications, in which databases with location-specific channel knowledge are used to fa…
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…