Curling Tactics Analysis with Reinforcement Learning
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Date
2025
Publication Type
Master Thesis
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yes
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Abstract
Curling is often referred to as "Chess on Ice" due to its tactical complexity. Featuring a continuous state and action space along with a complex, stochastic state transition, curling poses significant challenges for analytical modeling. In this thesis, we develop a self-supervised approach to learning curling tactics by combining backward induction with an actor-critic reinforcement learning method. Leveraging the finite horizon of the game, we apply backward induction to reduce the computational complexity. Each stage is solved using the deep deterministic policy gradient (DDPG) method to learn both a policy (actor) and a Q-function (critic). The best learned policies demonstrate consistent performance in a simulated tournament, especially when playing with the advantage of the last rock. Beyond policy learning, the Q-function approximation enables a quantitative evaluation of different tactical options, e.g., in an after-game review or during training.
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Examiner: Bolognani, Saverio
Examiner: Karapetyan, Aren
Examiner : Cederle, Matteo
Examiner: Dörfler, Florian
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ETH Zurich
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09478 - Dörfler, Florian / Dörfler, Florian