Generalized model learning for Reinforcement Learning on a humanoid robot

— Reinforcement learning (RL) algorithms have long been promising methods for enabling an autonomous robot to improve its behavior on sequential decision-making tasks. The obvious enticement is that the robot should be able to improve its own behavior without the need for detailed step-by-step programming. However, for RL to reach its full potential, the algorithms must be sample efficient: they must learn competent behavior from very few real-world trials. From this perspective, model-based methods, which use experiential data more efficiently than model-free approaches, are appealing. But they often require exhaustive exploration to learn an accurate model of the domain. In this paper, we present an algorithm, Reinforcement Learning with Decision Trees (RLDT), that uses decision trees to learn the model by generalizing the relative effect of actions across states. The agent explores the environment until it believes it has a reasonable policy. The combination of the learning appr...

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Todd Hester, Michael Quinlan, Peter Stone
• Keywords: Algorithm, Decision Trees, ICRA 2010, Robot, Robotics