The field of multi-agent reinforcement learning is moving towards more efficient and adaptive methods for handling complex, high-stakes scenarios. Researchers are exploring the use of latent variable modeling, expectation-maximization, and deep reinforcement learning to improve decentralized decision-making in areas such as wildlife protection and urban mobility regulation. Noteworthy papers include the introduction of a novel Expectation-Maximization based latent variable modeling approach for UAV coordination in wildlife protection, and the development of a symmetry-guided multi-agent inverse reinforcement learning framework that enhances sample efficiency. These innovative approaches demonstrate the potential for significant advancements in the field, enabling more effective and sustainable solutions for real-world problems.
Advances in Multi-Agent Reinforcement Learning
Sources
Latent Variable Modeling in Multi-Agent Reinforcement Learning via Expectation-Maximization for UAV-Based Wildlife Protection
Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning
Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
Toward a Metrology for Artificial Intelligence: Hidden-Rule Environments and Reinforcement Learning
Group Effect Enhanced Generative Adversarial Imitation Learning for Individual Travel Behavior Modeling under Incentives
Symmetry-Guided Multi-Agent Inverse Reinforcement Learnin
Symmetry-Guided Multi-Agent Inverse Reinforcement Learning