Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects

M Hua, D Chen, X Qi, K Jiang, ZE Liu, Q Zhou… - arXiv preprint arXiv …, 2023 - arxiv.org
Connected and automated vehicles (CAVs) have emerged as a potential solution to the
future challenges of developing safe, efficient, and eco-friendly transportation systems …

When to switch: planning and learning for partially observable multi-agent pathfinding

A Skrynnik, A Andreychuk, K Yakovlev… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Multi-agent pathfinding (MAPF) is a problem that involves finding a set of non-conflicting
paths for a set of agents confined to a graph. In this work, we study a MAPF setting, where …

Credit assignment in heterogeneous multi-agent reinforcement learning for fully cooperative tasks

K Jiang, W Liu, Y Wang, L Dong, C Sun - Applied Intelligence, 2023 - Springer
Credit assignment poses a significant challenge in heterogeneous multi-agent
reinforcement learning (MARL) when tackling fully cooperative tasks. Existing MARL …

Graph Exploration for Effective Multiagent Q-Learning

A Zhaikhan, AH Sayed - IEEE Transactions on Neural …, 2024 - ieeexplore.ieee.org
This article proposes an exploration technique for multiagent reinforcement learning (MARL)
with graph-based communication among agents. We assume that the individual rewards …