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MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning
Unlike existing models, MABL is capable of encoding essential global information into the latent states during training while guaranteeing the decentralized execution of learned policies.
Evolutionary Approach to Security Games with Signaling
Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife.
Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty
We therefore first propose a novel GSG model that combines defender allocation, patrolling, real-time drone notification to human patrollers, and drones sending warning signals to attackers.
Decision Making
Multiagent Systems
