Strategy Design for Card Games Based on Reinforcement Learning

This paper introduces a simple blackjack game environment built by the Gym library and explores how to solve this problem with the reinforcement learning algorithms. By simulating the environment and theoretical foundations, this paper also demonstrates how reinforcement learning can be used to train an agent to learn the optimal game strategy. The results show that the loss value fluctuates significantly in the initial training phase, but eventually stabilizes around 0.95, indicating the algorithm's convergence towards an optimal strategy. The current program has some limitations in multi- player games support, distributed computing support, and advanced algorithm integration. Future optimization directions should introduce multi-player support, optimize distributed computing support, and integrate advanced algorithms to improve algorithm performance and efficiency, thus handling more complex state and action spaces.