Deep reinforcement learning in continuous action spaces: A case study in the game of simulated curling

Many real-world applications of reinforcement learning require an agent to select optimal actions from continuous action spaces. Recently, deep neural networks have successfully been applied to games with discrete actions spaces. However, deep neural networks for discrete actions are not suitable for devising strategies for games in which a very small change in an action can dramatically affect the outcome. In this paper, we present a new framework which incorporates a deep neural network that can be used to learn game strategies based on a kernel-based Monte Carlo tree search that finds actions within a continuous space. To avoid hand-crafted features, we train our network using supervised learning followed by reinforcement learning with a high-fidelity simulator for the Olympic sport of curling. The program trained under our framework outperforms existing programs equipped with several hand-crafted features and won an international digital curling competition.