Learning to play the Game of Hearts using Reinforcement Learning and a Multi-Layer Perceptron
Martijn Wagenaar · 2017
The multi-agent card game Hearts is learned by the use of Monte Carlo learning and a Multilayer Perceptron. This card game has imperfect state information and is therefore harder to learn than perfect information games. A few different parameters will be looked at to attempt to find out which combination is most promising. Most importantly, two activation functions, namely Sigmoid and a Leaky version of the Rectified Linear Unit (ReLU), will be compared and a variation in the amount of hidden layers will be studied. After experimentation it is concluded that the Sigmoid function is outperformed by the ReLU. Multiple hidden layers seem to slow the learning process down and do not improve performance.