Quantile of Means: A Bonus-Free Ensemble Method for Minimax Optimal Reinforcement Learning
Researchers have introduced a novel ensemble method for reinforcement learning (RL) that offers theoretical guarantees for exploration without relying on traditional uncertainty estimates. This new approach, termed "Quantile of Means," is designed for finite-horizon Markov Decision Processes and provides optimal variance-dependent regret bounds. By offering a count-free method, it aims to provide a more practical and theoretically grounded way to implement ensemble-based exploration in RL. AI
IMPACT Provides a theoretically grounded, count-free approach for ensemble-based exploration in reinforcement learning, potentially simplifying practical implementations.