Fully Decentralized Cooperative Multi-agent Reinforcement Learning: A Survey

Abstract

Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner. Due to the lack of information about other agents, it is challenging to derive algorithms that can converge to the optimal joint policy in a fully decentralized setting. Thus, this research area has not been thoroughly studied. In this paper, we seek to systematically review the fully decentralized methods in two settings: maximizing a shared reward of all agents and maximizing the sum of individual rewards of all agents, and discuss open questions and future research directions.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyjiang2024fully

Related papers

• A Review Of Cooperative Multi-agent Deep Reinforcement Learning (2019)19.08
• Optimization For Reinforcement Learning: From Single Agent To Cooperative Agents (2019)14.62
• Deep Multiagent Reinforcement Learning: Challenges And Directions (2021)0.00
• Multi-agent Reinforcement Learning: A Report On Challenges And Approaches (2018)0.00
• Multi-agent Reinforcement Learning: A Comprehensive Survey (2023)0.00
• An Initial Introduction To Cooperative Multi-agent Reinforcement Learning (2024)0.00
• Decentralized Multi-agent Reinforcement Learning With Networked Agents: Recent Advances (2019)0.00
• From Centralized To Self-supervised: Pursuing Realistic Multi-agent Reinforcement Learning (2023)0.00