Watch and Act: Dual Interacting Agents for Automatic Generation of Possession Statistics in Soccer

Abstract

Pass localization and team identification are two primary tasks for pass-count based possession statistics generation of a soccer match. While the existing works perform these two tasks separately, we propose dual interacting reinforcement learning agents to jointly perform these tasks. The proposed model has a localization agent, that decides which direction to move a temporal window to localize a pass. On the other hand, there is an identification agent that decides if the temporal window contains a pass for team-A (or team-B), or the localization agent needs to readjust the temporal window further. In this multi-agent setup, an agent may communicate by sharing some message to guide the other agent to achieve its task. To achieve this inter-agent communication, we extend the Dueling DQN architecture and share the value of a state as a message to the other agent. Two agents watch, act independently and cooperate with each other in order to detect a valid pass in a soccer video. A novel reward function is proposed that helps the agents to learn the optimal policy. Experiments performed on online videos show that our method is 3% better at localization of pass than the competitive methods.