ReLiable:Offline Reinforcement Learning for Tactical Strategies in Professional Basketball Games

Professional basketball provides an intriguing example of a dynamic spatio-temporal game that incorporates both hidden strategy policies and situational decision making. During a game, the coaches and players are assumed to follow a general game plan, but players are also forced to make spur-of-the-moment decisions based on immediate conditions on the court. However, because it is challenging to process heterogeneous signals on the court and the space of potential actions and outcomes is massive, it is hard for players to find an optimal strategy on the fly given a short amount of time to observe conditions and take action. In this work, we present ReLiable (ReinforcemEnt Learning In bAsketBaLl gamEs). Specifically, we investigate the possibility of using reinforcement learning (RL) to guide player decisions. We train an offline deep Q-network (DQN) on historical National Basketball Association (NBA) game data from 2015-2016. The data include play-by-play and player movement sensor data. We apply our trained agent to games that it has not seen. Our method is able to propose potentially smarter tactical strategies, compared with replay gameplay data, producing expected final game scores comparable to elite NBA teams. Our approach can be useful for learning strategy policies from other game-like domains characterized by competing groups and sequential spatio-temporal event data.