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Soccer is a sparse rewarding game: any smart or careless action in criticalsituations can change the result of the match. Therefore players, coaches, andscouts are all curious about the best action to be performed in criticalsituations, such as the times with a high probability of losing ball possessionor scoring a goal. This work proposes a new state representation for the soccergame and a batch reinforcement learning to train a smart policy network. Thisnetwork gets the contextual information of the situation and proposes theoptimal action to maximize the expected goal for the team. We performedextensive numerical experiments on the soccer logs made by InStat for 104European soccer matches. The results show that in all 104 games, the optimizedpolicy obtains higher rewards than its counterpart in the behavior policy.Besides, our framework learns policies that are close to the expected behaviorin the real world. For instance, in the optimized policy, we observe that someactions such as foul, or ball out can be sometimes more rewarding than a shotin specific situations.

Towards optimized actions in critical situations of soccer games with deep reinforcement learning