MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems

2022-10-26 · AAAI -22 2022 10 · Zefang Zong, Meng Zheng, Yong Li, Depeng Jin

Cooperative Pickup and Delivery Problem (PDP), as a variant of the typical Vehicle Routing Problems (VRP), is an important formulation in many real-world applications, such as on-demand delivery, industrial warehousing, etc. It is of great importance to efficiently provide high-quality solutions of cooperative PDP. However, it is not trivial to provide effective solutions directly due to two major challenges: 1) the structural dependency between pickup and delivery pairs require explicit modeling and representation. 2) the cooperation between different vehicles is highly related to solution exploration and is difficult to model. In this paper, we propose a novel multi-agent reinforcement learning-based framework to solve the cooperative PDP (MAPDP). First, we design a paired context embedding to well measure the dependency of different nodes considering their structural limits. Second, we utilize cooperative multi-agent decoders to leverage the decision dependence among different vehicle agents based on a special communication embedding. Third, we design a novel cooperative A2C algorithm to train the integrated model. We conduct extensive experiments on a randomly generated dataset and a real-world dataset. Experiments result shown that the proposed MAPDP outperforms all other baselines by at least 1.64% in all settings, and shows significant computation speed during solution inference.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning

Developing cooperative policies for multi-stage reinforcement learning tasks

2022-05-11 · Jordan Erskine, Chris Lehnert

Many hierarchical reinforcement learning algorithms utilise a series of independent skills as a basis to solve tasks at a higher level of reasoning. These algorithms don't consider the value of using skills that are coop…

Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation

2021-03-08 · Abhimanyu Dubey, Alex Pentland

Reinforcement learning in cooperative multi-agent settings has recently advanced significantly in its scope, with applications in cooperative estimation for advertising, dynamic treatment regimes, distributed control, an…

Federated Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Developing cooperative policies for multi-stage tasks

2020-07-01 · Jordan Erskine, Chris Lehnert

This paper proposes the Cooperative Soft Actor Critic (CSAC) method of enabling consecutive reinforcement learning agents to cooperatively solve a long time horizon multi-stage task. This method is achieved by modifying …

Reinforcement Learning (RL)

Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective

2024-08-25 · Qi Liu, Jianqi Gao, Dongjie Zhu, Zhongjian Qiao 외

Multi-agent target assignment and path planning (TAPF) are two key problems in intelligent warehouse. However, most literature only addresses one of these two problems separately. In this study, we propose a method to si…

Deep Reinforcement Learning Reinforcement Learning (RL)

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey

2024-01-10 · Jiechuan Jiang, Kefan Su, Zongqing Lu

Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner. D…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Survey