Greedy Policy

Greedy policies, which select the seemingly best option at each step, are a fundamental approach in reinforcement learning and related fields like combinatorial optimization. Current research focuses on improving the efficiency and convergence of greedy algorithms, particularly through techniques like multi-step lookahead, adaptive sampling, and incorporating them into broader frameworks such as policy mirror descent and Thompson sampling. These advancements aim to address limitations of traditional greedy approaches, such as slow convergence or poor performance in high-dimensional spaces, leading to more efficient and robust solutions for various applications including robotics, active learning, and Bayesian optimization.