Deep Reinforcement Learning for Card Games

Sammanfattning: This project aims to investigate how reinforcement learning (RL) techniques can be applied to the card game LimitTexas Hold’em. RL is a type of machine learning that can learn to optimally solve problems that can be formulated according toa Markov Decision Process.We considered two different RL algorithms, Deep Q-Learning(DQN) for its popularity within the RL community and DeepMonte-Carlo (DMC) for its success in other card games. With the goal of investigating how different parameters affect their performance and if possible achieve human performance.To achieve this, a subset of the parameters used by these methods were varied and their impact on the overall learning performance was investigated. With both DQN and DMC we were able to isolate parameters that had a significant impact on the performance.While both methods failed to reach human performance, both showed obvious signs of learning. The DQN algorithm’s biggest flaw was that it tended to fall into simplified strategies where it would stick to using only one action. The pitfall for DMC was the fact that the algorithm has a high variance and therefore needs a lot of samples to train. However, despite this fallacy,the algorithm has seemingly developed a primitive strategy. We believe that with some modifications to the methods, better results could be achieved.