The efficacy of symmetric cognitive biases in robotic motion learning

We propose an application of human-like decision-making to robotic motion learning. Human is known to have illogical symmetric cognitive biases that induce “if p then q” and “if not q then not p” from “if q then p.” The loosely symmetric Shinohara model quantitatively represents the tendencies (Shinohara et al. 2007). Previous studies one of the authors have revealed that an agent with the model used as the action value function shows great performance in n-armed bandit problems, because of the illogical biases. In this study, we apply the model to reinforcement learning with Q-learning algorithm. Testing the model on a simulated giant-swing robot, we have confirmed its efficacy in convergence speed increase and avoidance of local optimum.