Reinforcement Learning Strategies for the Card Game 'Getaway' An Evaluation of Q-learning and SARSA

This paper investigates the application of model-free reinforcement learning algorithms to optimize an AI agent for the card game 'Getaway'. Specifically, we focus on two temporal difference learning methods: Q-learning for off-policy learning and SARSA for on-policy learning. We compare the policies derived from both algorithms, demonstrating that the Q-learning approach is more suited to the strategic requirements of 'Getaway'. Additionally, we introduce a novel adaptation to Q-learning that involves seeding initial Q-values for each state-action pair following extensive exploration. Our analysis details how this modification enhances the performance of the model, offering insights into its potential for broader applications in complex game environments.