Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges

Guanjun Liu; Ying Tang; Ziyuan Zhou

Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2305.10091 v1 pith:QHCDCYQR submitted 2023-05-17 cs.AI

Ziyuan Zhou , Guanjun Liu , Ying Tang This is my paper · ORCID

classification cs.AI

keywords marlapplicationsmethodsresearchapplicationchallengesdecadeinteraction

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. However, current studies and applications need to address its scalability, non-stationarity, and trustworthiness. This paper aims to review methods and applications and point out research trends and visionary prospects for the next decade. First, this paper summarizes the basic methods and application scenarios of MARL. Second, this paper outlines the corresponding research methods and their limitations on safety, robustness, generalization, and ethical constraints that need to be addressed in the practical applications of MARL. In particular, we believe that trustworthy MARL will become a hot research topic in the next decade. In addition, we suggest that considering human interaction is essential for the practical application of MARL in various societies. Therefore, this paper also analyzes the challenges while MARL is applied to human-machine interaction.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Learning To Communicate Over An Unknown Shared Network
cs.MA 2025-07 conditional novelty 6.0 of 10

A DRL-based querying policy trained only on a single-parameter queue simulation transfers zero-shot to real WiFi (5-50 agents) and cellular networks and adapts its query rate to congestion.
Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security
cs.CR 2025-06 reject novelty 5.0 of 10

The paper proposes a co-evolving red-blue reinforcement learning environment for network intrusion detection and claims the blue agent recovers up to 30% accuracy after just 2 to 3 adaptation steps with 25 to 30 sampl...
Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
cs.LG 2025-07 conditional novelty 4.0 of 10

In a simulated supply chain driven by a fitted demand model, MARL pricing agents earn far higher revenue than rule-based agents while reducing fairness and stability.

Pith tools