Among all the popular sports, soccer is a relatively long-lasting game with a small number of goals per game. This renders the decision-making cumbersome, since it is not straightforward to evaluate the impact of in-game actions apart from goal scoring. Although several action valuation metrics and counterfactual reasoning have been proposed by researchers in recent years, assisting coaches in discovering the optimal actions in different situations of a soccer game has received little attention of soccer analytics. This work proposes the application of deep reinforcement learning on the event and tracking data of soccer matches to discover the most impactful actions at the interrupting point of a possession. Our optimization framework assists players and coaches in inspecting the optimal action, and on a higher level, we provide for the adjustment required for the teams in terms of their action frequencies in different pitch zones. The optimization results have different suggestions for offensive and defensive teams. For the offensive team, the optimal policy suggests more shots in half-spaces (i.e. long-distance shots). For the defending team, the optimal policy suggests that when locating in wings, defensive players should increase the frequency of fouls and ball outs rather than clearances, and when located in the centre, players should increase the frequency of clearances rather than fouls and ball outs.
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