Multi-agent deep reinforcement learning: a survey ...
This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning. We focus primarily on literature from recent years that combines deep …
Multi-agent reinforcement learning - Wikipedia
Multi-agent reinforcement learning is closely related to game theory and especially repeated games, as well as multi-agent systems. Its study combines the pursuit of finding ideal algorithms that maximize …
Multi-agent deep reinforcement learning for group ...
Dec 8, 2025 · Multi-agent deep reinforcement learning (MADRL) enables agents to learn and optimize their policies through interactions within a shared environment, addressing both cooperation and …
Multi-agent Reinforcement Learning: A Comprehensive Survey
Dec 15, 2023 · This survey examines these challenges, placing an emphasis on studying seminal concepts from game theory (GT) and machine learning (ML) and connecting them to recent …
A multi-agent reinforcement learning framework for exploring ...
Dec 8, 2025 · The authors propose a multi-agent reinforcement learning approach to exploring complex decision-making. They uncover the memory-two bilateral reciprocity strategy that outperforms a wide …
Asynchronous multi-agent deep reinforcement learning under ...
Feb 6, 2025 · Multi-agent reinforcement learning (MARL) is a promising framework to generate solutions for these kinds of multi-robot problems. Recently, by leveraging deep neural networks to deal with …
A Review of Multi-Agent Reinforcement Learning Algorithms
Feb 19, 2025 · Through this discussion, readers can gain a comprehensive understanding of the current research status and future trends in multi-agent reinforcement learning algorithms, providing valuable …