Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

TL;DR

This paper introduces a hybrid multi-agent deep reinforcement learning approach combining Soft Actor-Critic and bipartite matching to improve autonomous mobility on demand systems, demonstrating superior performance on real-world data.

Contribution

It presents a novel combination of multi-agent DRL and bipartite matching for anticipative request assignment in autonomous mobility systems.

Findings

Outperforms state-of-the-art benchmarks in real-world data tests.

Achieves better stability and computational efficiency.

Provides a coordinated decision-making framework for autonomous mobility.

Abstract

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision process and propose a novel combination of multi-agent Soft Actor-Critic and weighted bipartite matching to obtain an anticipative control policy. Thereby, we factorize the operator's otherwise intractable action space, but still obtain a globally coordinated decision. Experiments based on real-world taxi data show that our method outperforms state of the art benchmarks with respect to performance, stability, and computational tractability.