Reward Feedback

Reward feedback, crucial for training intelligent agents, is a central focus in reinforcement learning research, aiming to optimize how agents learn from feedback signals to improve performance. Current research emphasizes efficient learning from various feedback types, including noisy preferences, delayed or composite rewards, and even indirect feedback derived from mutual information maximization, employing algorithms like EXP3 variants and posterior sampling methods within bandit and Markov Decision Process frameworks. These advancements are improving the robustness and efficiency of reinforcement learning in diverse applications, from robotics and personalized advertising to generative AI model fine-tuning.