An extended Q learning system with emotion state to make up an agent with individuality

Recently, researches for the intelligent robots incorporating knowledge of neuroscience have been actively carried out. In particular, a lot of researchers making use of reinforcement learning have been seen, especially, “Reinforcement learning methods with emotions”, that has already proposed so far, is very attractive method because it made us possible to achieve the complicated object, which could not be achieved by the conventional reinforcement learning method, taking into account of emotions. In this paper, we propose an extended reinforcement (Q) learning system with amygdala (emotion) models to make up individual emotions for each agent. In addition, through computer simulations that the proposed method is applied to the goal search problem including a variety of distinctive solutions, it finds that each agent is able to have each individual solution.