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Wednesday, April 5, 2017

Talk summary 2: Modeling Sequential Decision Making in Team Sports using Imitation Learning

Dr. Peter Carr is working as a research scientist at Disney Research, Pittsburgh. In this talk, he introduced their recent work about modeling sequential decision making in sports using imitation learning techniques. I would like to summarize his talk from the following three critical points.

What is the objective of their work?

In terms of team sports analysis, researchers try to compare the performance of a specific teams or players with that of a typical team in a professional league, i.e. average league performance. To be able to quantitatively study players' movement patterns, it requires existence of player tracing dataset. Fortunately, with the advance of data collection techniques in recent years, it becomes possible that people gather spatiotemporal sport data by tracing players' movements in a large number of games. In the talk, Dr. Peter Carr mentioned that they have used approximately 100 games of player tracking data from a professional soccer league for modeling sequential decision making.

With such dataset, they are interested in modeling defensive situations - what players would do within under the situation where the opposition had control of the ball. They explore what a defensive player should have done, based on the average league performance revealed by data, in comparison with what they actually did. This kind of work help us better understand the overall defensive strategies of a league as well as how a certain team would play differently. In this framework, the "should-have-done" motions are learned from player tracking data through "data-driven ghosting" method. In next section, we will summarize the high-level intuition of the method.

What is the method?

Data-driven ghosting is implemented based on imitation learning. Imitation learning, also called "learning from demonstration", is a process that computer automatically learns strategies by observing expert behaviors. It is similar to what human would do in learning process - a person who has no knowledge of sports, can understand what to do if he/she has observed a sufficient number of games.

Figure 1. Deep multi-agent imitation learning framework. Single player learning (Upper) and multi-agent learning (Bottom).
Their task is to predict the action of a player at each time step given the state feature, which actually is a online sequence decision making problem. Besides, they need to predict actions for multiple players at the same time. Therefore, they proposed a deep multi-agent imitation learning framework (figure 1). Two major components are presented - single player modeling and joint training of multiple players. In stage 1, the algorithm learns a model for each player to predict average league action, and in stage 2, these pre-trained models learned in stage 1 would be used in stage 2 for joint training of multiple agents. In both stages, training and prediction are combined together, so that a model can learn from their prediction mistakes to go back to "right" track (see in figure 1).

How about the results?

An example is shown in figure 2. The data-driven ghosting players (white) and their trajectories (white) represent average league movements; colored dots and trajectories represent actual movements in games. Results have revealed that the proposed model can generate a sequence of behaviors showing spatial and formational awareness. More information can be viewed in the video:
Figure 2. Ghosting behaviors (white) in comparison of actual movements.

[1] Le, Hoang M., et al. "Data-Driven Ghosting using Deep Imitation Learning." (2017).

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