Multi Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) focuses on developing algorithms that enable multiple independent agents to learn optimal strategies within a shared environment, often to achieve a common goal. Current research emphasizes improving sample efficiency and generalization, exploring novel architectures like equivariant graph neural networks and specialized network structures (e.g., Bottom-Up Networks), and addressing challenges posed by non-stationarity and partial observability through techniques such as auxiliary prioritization and global state inference with diffusion models. MARL's significance lies in its potential to solve complex real-world problems across diverse domains, including robotics, traffic control, and healthcare, by enabling effective coordination and collaboration among multiple agents.
Papers
Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning
Liu Weiwei, Hu Wenxuan, Jing Wei, Lei Lanxin, Gao Lingping, Liu Yong
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making
Chitra Subramanian, Miao Liu, Naweed Khan, Jonathan Lenchner, Aporva Amarnath, Sarathkrishna Swaminathan, Ryan Riegel, Alexander Gray
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning for Digital Twins
Eslam Eldeeb, Houssem Sifaou, Osvaldo Simeone, Mohammad Shehab, Hirley Alves
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
Ayesha Siddika Nipu, Siming Liu, Anthony Harris
Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems
Tianzhang Cai, Qichen Wang, Shuai Zhang, Özlem Tuğfe Demir, Cicek Cavdar
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs
Abhishek Mondal, Deepak Mishra, Ganesh Prasad, George C. Alexandropoulos, Azzam Alnahari, Riku Jantti
$\widetilde{O}(T^{-1})$ Convergence to (Coarse) Correlated Equilibria in Full-Information General-Sum Markov Games
Weichao Mao, Haoran Qiu, Chen Wang, Hubertus Franke, Zbigniew Kalbarczyk, Tamer Başar
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints
Dan Qiao, Yu-Xiang Wang
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning
Benjamin Patrick Evans, Sumitra Ganesh
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management
Zhenglong Li, Vincent Tam, Kwan L. Yeung