Measuring the Effectiveness of Reinforcement Learning for Behavior-Based Robots

Abstract

We explore the use of behavior-based architectures within the context of reinforcement learning and examine the effects of using different behavior-based architectures on the ability to learn correctly and efficiently the task at hand. In particular, we study the task of learning to push boxes in a simulated two-dimensional environment originally proposed by Mahadevan and Connell (1992). We examine issues such as effectiveness of learning, flexibility of the learning method to adapt to new environments, and effect of the behavior architecture on the ability to learn, and we report results obtained on a large number of simulation runs.