ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

TL;DR

ScheduleNet is a reinforcement learning-based scheduler that learns to coordinate multiple agents in real-time to efficiently solve complex multi-agent scheduling problems like mTSP and JSP.

Contribution

It introduces a novel RL framework with a graph attention mechanism for decentralized decision-making in multi-agent scheduling tasks.

Findings

ScheduleNet effectively solves various multi-agent scheduling problems.

It outperforms traditional heuristics in multiple scenarios.

The approach generalizes across different problem types.

Abstract

We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic reward (makespan) and learn ScheduleNet, a decentralized decision-making policy that can effectively coordinate multiple agents to complete tasks. The decision making procedure of ScheduleNet includes: (1) representing the state of a scheduling problem with the agent-task graph, (2) extracting node embeddings for agent and tasks nodes, the important relational information among agents and tasks, by employing the type-aware graph attention (TGA), and (3) computing the assignment probability with the computed node embeddings. We validate the effectiveness of ScheduleNet as a general learning-based scheduler for solving various types of multi-agent scheduling tasks, including multiple salesman traveling problem (mTSP) and job shop scheduling problem (JSP).