Deep Reinforcement Learning Agents for Decision Making for Gameplay

Robots are becoming more integrated into society as they become more advanced, and the programming behind them needs to continue to progress in order for the robots to be utilized to their fullest potential. Artificial Intelligence (AI) is one of the most versatile and quickly growing areas of robotic control, and has been used for a variety of different robots and tasks. One potential use of robotics and AI is in that of childhood development. Cooperative play has been shown to be a crucial part of childhood development, and for children with developmental disabilities, playing with other children may be difficult and frustrating, leading them to miss out on this important milestone. Cooperative play with robots has been shown to have positive educational and therapeutic effects on children with developmental disabilities, and so robots can be used as substitute players for children who have troubles playing with other children. To achieve this, AI algorithms must be developed that can make appropriate decisions or moves for a given game, to such an extent that the children would choose to play with the robot instead of alone. In this paper two AI agents are developed to play Menara, a cooperative tower building game. The two agents include a pillar placement agent and a tile placement agent. They implement algorithms including the method for selecting the pillars to have available to the agent during gameplay, and how many pillars the agent plans to place in a single turn. The tile placement agent was able to successfully balance a tile 62% of the time, while the pillar placement agent was able to succeed 88% of the time on the test dataset.