Markovian Policy

Markovian policies, where actions depend only on the current state, are a cornerstone of reinforcement learning, enabling efficient planning and learning algorithms. Current research focuses on extending their applicability to more complex scenarios, including those with stochastic delays, budgetary constraints, and imperfect observations, often employing techniques like Q-learning, policy gradient methods, and Monte Carlo tree search to optimize policy performance. These advancements are crucial for addressing real-world challenges in diverse fields such as robotics, economics, and natural language processing, where agents must make decisions under uncertainty and constraints. The development of efficient algorithms for handling non-Markovian policies and their analysis through occupancy measures is also a significant area of investigation.