34 citations · 56 across the 6 of their papers we have counts for
7 papers
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Dongjie Yu, Wenjun Zou, Yujie Yang +4
Safe reinforcement learning (RL) that solves constraint-satisfactory policies provides a promising way to the broader safety-critical applications of RL in real-world problems such…
Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning
Yuheng Lei, Jianyu Chen, Shengbo Eben Li +1
Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a s…
Flow-based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen, Yao Mu, Ping Luo +2
Partially Observable Markov Decision Process (POMDP) provides a principled and generic framework to model real world sequential decision making processes but yet remains unsolved,…
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning
Sheng Li, Yutai Zhou, Ross Allen +1
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication wh…
Analysis of Fleet Management and Network Design for On-Demand Urban Air Mobility Operations
Sheng Li, Maxim Egorov, Mykel J. Kochenderfer
A significant challenge in estimating operational feasibility of Urban Air Mobility (UAM) missions lies in understanding how choices in design impact the performance of a complex s…
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
Sheng Li, Jayesh K. Gupta, Peter Morales +2
Multi-agent reinforcement learning (MARL) requires coordination to efficiently solve certain tasks. Fully centralized control is often infeasible in such domains due to the size of…