On-Demand Transport (ODT) systems have attracted increasing attention in recent years. Traditional centralized dispatching can achieve optimal solutions, but NP-Hard complexity makes it unsuitable for online and dynamic problems. Centralized decentralized heuristics fast, feasible solution at run-time with no guarantee on the quality. Starting from a not solution, we present this paper new mode...

Cooperative Multi-agent Reinforcement Learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via solving MARL-specific challenges (e.g., non-stationarity, credit assignment, scalability), but ignore the policy perturbation issue when testing in a different environment. This hasn't been considered problem formula...

Recently, some challenging tasks in multi-agent systems have been solved by hierarchical reinforcement learning methods. Inspired the intra-level and inter-level coordination human nervous system, we propose a novel value decomposition framework HAVEN based on for fully cooperative problems. To address instability arising from concurrent optimization of policies between various levels agents, i...

The Foundation for Intelligent Physical Agents (FIPA) provides a rich set of standards for implementing industrial scale multi-agent infrastructures. Despite its manifold possibilities for achieving coordinated action execution based on interaction protocols, it still lacks direct support of multi-agent planning and scheduling for goal directed action execution. In this paper, we discuss a desi...