Beyond action valuation: A deep reinforcement learning framework for optimizing player decisions in soccer

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Pegah Rahimian, Jan Van Haaren, Togzhan Abzhanova, Laszlo Toka


This paper proposes an end-to-end deep reinforcement learning framework that receives raw tracking data for each situation in a game, and yields optimal ball destination location on the full surface of the pitch. Using the proposed approach, soccer players and coaches are able to analyze the actual behavior in their historical games, obtain the optimal behavior and plan for future games, and evaluate the outcome of the optimal decisions prior to deployment in a match.