Finite-State Controllers for (Hidden-Model) POMDPs using Deep Reinforcement Learning

This research presents a method for designing finite-state controllers for Partially Observable Markov Decision Processes (POMDPs) by employing deep reinforcement learning. This approach is critical for creating autonomous agents that can make optimal decisions in environments where only partial information is available, with applications in robotics, autonomous navigation, and intelligent control systems.