LPMARL: Linear Programming based Implicit Task Assignment for Hierarchical Multi-agent Reinforcement LearningDownload PDF

Anonymous

22 Sept 2022, 12:33 (modified: 15 Nov 2022, 12:19)ICLR 2023 Conference Blind SubmissionReaders: Everyone
Keywords: Linear programming, Multi-agent reinforcement learning, Hierarchical multi-agent reinforcement learning, Implicit deep learning
TL;DR: Linear programming-based optimal agent-task allocation for hierarchical multi-agent reinforcement learning.
Abstract: Training a multi-agent reinforcement learning (MARL) model with sparse reward is notoriously difficult because the terminal reward is induced by numerous interactions among agents. In this study, we propose linear programming-based hierarchical MARL (LPMARL) to learn effective coperative strategy among agents. LPMARL is composed of two hierarchical decision-making schemes: (1) solving an agent-task assignment problem and (2) solving a local cooperative game among agents that are assigned to the same task. For the first step, LPMARL formulates the agent-task assignment problem as linear programming (LP) using the state-dependent cost parameters generated by a graph neural network (GNN). Solving the LP can be considered as assigning tasks to agents, which decomposes the original problem into a set of task-dependent sub-problems. After solving the formulated LP, LPMARL employs a general MARL strategy to derive a lower-level policy to solve each sub-task in a cooperative manner. We train the LP-parameter generating GNN layer and the low-level MARL policy network, which are the essential components for making hierarchical decisions, in an end-to-end manner using the implicit function theorem. We empirically demonstrate that LPMARL learns an optimal agent-task allocation and the subsequent local cooperative control policy among agents in sub-groups for solving various mixed cooperative-competitive environments.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)
16 Replies

Loading