Imitating play from game trajectories: Temporal difference learning versus preference learning

This work compares the learning of linear evaluation functions using preference learning versus least squares temporal difference learning, LSTD(λ), from samples of game trajectories. The game trajectories are taken from human competitions held by the French Othello Federation¹. The raw board positions are used to create a linear evaluation function to illustrate the key difference between the two learning approaches. The results show that the policies learned, using exactly the same game trajectories, can be quite different. For the simple set of features used, preference learning produces policies that better capture the behaviour of expert players, and also lead to higher levels of play when compared to LSTD(λ).