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 - Page 6
Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation
Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model
Learning to Cooperate with Humans using Generative Agents
Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable