Generalized Circle Agent for Geometry Friends Using Deep Reinforcement Learning

Reinforcement learning began to perform at human-level success in game intelligence after deep learning revolution. Geometry Friends is a puzzle game, where we can benefit from deep learning and expect to have successful game playing agents. In the game, agents are collecting targets in two dimensional environment and they try to overcome obstacles in the way. In this paper, Q-learning approach is applied to this game and a generalized circle agent for different types of environment is implemented. Agent is trained by giving only screen pixels as input via a Convolutional Neural Network. Experimental results show that with the proposed method game completion rate and completion times are improved compared to random agent.