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

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

URL: http://arxiv.org/abs/2106.03051v1
Date: Sun, 6 Jun 2021 07:08:58 GMT
Title: ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning
Authors: Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
Abstract summary: We learn ScheduleNet, a decentralized decision-making policy that can effectively coordinate multiple agents to complete tasks. We validate the effectiveness of ScheduleNet as a general learning-based scheduler for solving various types of multi-agent scheduling tasks.
Score: 10.16257074782054
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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).

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