IEEE Access • Vol 12
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing
January 2024 • Hadar Szostak, Kobi Cohen
We consider a decentralized formulation of the active hypothesis testing (AHT) problem, where multiple agents gather noisy observations from the environment with the purpose of identifying the correct hypothesis. At each time step, agents have the option to select a sampling action. These different actions result in observations drawn from various distributions, each associated with a specific hypothesis. The agents collaborate to accomplish the task, where message exchanges between agents are allowed over a rate-…