Deep Imitation Learning on Spatio-Temporal Data with Multiple Adversarial Agents Applied on Soccer
Abstract: Recently, the availability of high quality and high resolution spatio-temporal data has increased for many sports. This enabled deep analysis of player behaviour and game strategy. This thesis investigates the assumption that game strategy is latent information in tracking data from soccer games and the possibility of modelling player behaviour with deep imitation learning. A possible application would be to perform counterfactual analysis, and switch an observed player in a real sequence, with a simulated player to asses alternative scenarios. An imitation learning application is implemented using recurrent neural networks. It is shown that the application is able to learn individual player behaviour and perform rollouts on previously unseen sequences.
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