MDP Model

Markov Decision Processes (MDPs) model sequential decision-making under uncertainty, aiming to find optimal policies maximizing cumulative rewards. Current research focuses on addressing challenges like model uncertainty (e.g., using multi-model MDPs and robust reinforcement learning), improving sample efficiency through abstract state representations and efficient algorithms, and handling complex real-world scenarios with heterogeneous environments and non-Markovian safety constraints. These advancements are crucial for improving the performance and reliability of reinforcement learning agents in various applications, from robotics and healthcare to online advertising and autonomous systems.