Junkang Liang — accepted AI-conference papers

2024

Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks

Pu Feng, Junkang Liang, Size Wang, Xin Yu, Xin Ji, Yiting Chen +3

IROS 2024poster

In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in training versus reliance on local observations in execution, lacking global signals. Inspired by human societal consens…

Cited by 6 Source ↗Scholar ↗

2024

Safe and Efficient Multi-Agent Collision Avoidance With Physics-Informed Reinforcement Learning

Pu Feng, Rongye Shi, Size Wang, Junkang Liang, Xin Yu, Simin Li +1

RA-L 2024

Reinforcement learning (RL) has shown great promise in addressing multi-agent collision avoidance challenges. However, existing RL-based methods often suffer from low training efficiency and poor action safety. To tackle these issues, we introduce a physics-informed reinforcement learning framework

Cited by 12 Source ↗Scholar ↗