机器学习与数据科学博士生系列论坛（第五十五期）—— A Distributional Perspective on Reinforcement Learning

摘要：
Distributional reinforcement learning (DRL) is a new mathematical framework for sequential decision making, which has achieved success in various fields such as risk management and computational neuroscience. Going beyond the expectation of cumulative return G=\sum_t \gamma^t R_t considered in classical RL, DRL focuses on the probability distribution of G. This allows us to define agents' behaviors that depend on the full distributions of G. For example, agents may avoid states that carry a high probability of failure or penalize decisions that have high variance. However, distirbution is an infinite-dimensional object, which poses new challenges for algorithm design and theoretical analysis. In this seminar, we follow the recent book, "Distributional reinforcement learning", to give a concise overview of the basic concepts, theoretical framework, and practical algorithms.

论坛简介：该线上论坛是由张志华教授机器学习实验室组织，每两周主办一次（除了公共假期）。论坛每次邀请一位博士生就某个前沿课题做较为系统深入的介绍，主题包括但不限于机器学习、高维统计学、运筹优化和理论计算机科学。