Reward Distribution

Reward distribution in reinforcement learning focuses on accurately modeling and utilizing the probability distribution of rewards, rather than just their expected value, to improve decision-making. Current research emphasizes efficient algorithms for approximating these distributions, particularly in complex scenarios with continuous or heavy-tailed rewards, using techniques like distributional dynamic programming and quantile-spline discretizations. This improved understanding of reward distributions is crucial for enhancing the robustness and generalization capabilities of reinforcement learning agents across diverse applications, including vision-language models and multi-armed bandit problems. Furthermore, research is actively exploring how to leverage information about reward distribution changes over time to improve adaptation and reduce regret in dynamic environments.