Student seminar series
David Awosoga
PhD Candidate, University of ݮƵ
Room: M3 3127
Applied Bayesian Reinforcement Learning for Credit Assignment in Volleyball
Understanding individual contribution towards overall group output, otherwise known as credit assignment, is a complex and multifaceted area of study relevant across a wide domain of fields. Various methods have been proposed to quantify contribution based on recorded actions and active participation, but an understudied area of credit assignment is in investigating the optimality of individual decision-making. One area of application is in team sports, where effectively assigning credit to players allows coaches to optimally construct rosters, allocate playing time, and improve upon strategic and tactical considerations. This work focuses on volleyball, where the most influential decisions are those made by the “setter”, who is responsible for distributing attack opportunities among their teammates. Drawing inspiration from Bayesian reinforcement learning strategies for addressing sequential decision-making under uncertainty, this talk will propose an improved setter evaluation framework.