The field of multi-agent reinforcement learning (MARL) is rapidly advancing, with a focus on improving the ability of agents to learn and adapt in complex, dynamic environments. Recent developments have centered on addressing the challenges of knowledge transfer, decentralized decision-making, and safe multi-agent motion planning. Researchers are exploring innovative approaches, such as causal knowledge transfer frameworks and neural Hamilton-Jacobi reachability learning, to enable agents to better generalize and adapt to new situations. Notable papers in this area include the introduction of the Neural Hamilton-Jacobi Reachability Learning (HJR) method for decentralized safe multi-agent motion planning, which provides scalable neural HJR modeling to tackle high-dimensional configuration spaces and capture worst-case collision and safety constraints between agents. Another noteworthy paper proposes the Multi-Agent Guided Policy Optimization (MAGPO) framework, which integrates centralized guidance with decentralized execution to better leverage centralized training and provide theoretical guarantees of monotonic policy improvement.
Advances in Multi-Agent Reinforcement Learning
Sources
Causal Knowledge Transfer for Multi-Agent Reinforcement Learning in Dynamic Environments
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms
COMPASS: Cooperative Multi-Agent Persistent Monitoring using Spatio-Temporal Attention Network
Multi-agent Reinforcement Learning for Robotized Coral Reef Sample Collection
Multi-Agent Guided Policy Optimization
Remembering the Markov Property in Cooperative MARL