CheckersMind: Enhancing Cognitive Ability in Dimentia Patients Through Checkers Game Therapy with Chatbot

In this study, explore how reinforcement learning could be used to improve the checkers-playing experience for people with dementia as part of a therapeutic gaming environment. In order to train an agent that can aid in both offensive and defensive strategies, the Soft Actor-Critic (SAC) reinforcement learning algorithm is used. The fundamental goal of this research is to find ways to stimulate the brains of people with dementia in order to improve their cognitive abilities, such as memory, problem-solving, and observational skills. The Deep Deterministic Policy Gradients (DDPG) and the Soft Actor-Critic reinforcement learning algorithms are compared and contrasted. The results show that the Soft Actor-Critic algorithm excels in this setting. This study adds to the growing body of evidence supporting the use of game-based therapies in the treatment of dementia, with the aim of improving patients' cognitive abilities and quality of life.