Implementation of an embodied general reinforcement learner on a serial link manipulator
BECCA (a Brain-Emulating Cognition and Control Architecture software package) was developed in order to perform general reinforcement learning, that is, to enable unmodeled embodied systems operating in unstructured environments to perform unfamiliar tasks. It accomplishes this through automatic paired feature creation and reinforcement learning algorithms. This paper describes an implementation of BECCA on a seven Degree of Freedom (DoF) Barrett Whole Arm Manipulator (WAM) undergoing a series of experiments designed to test the reinforcement learner's ability to adapt to the WAM hardware. In the experiments, the following is demonstrated, 1) learning to transition the WAM between states, 2) learning to perform at near optimal levels on one, two and three dimensional navigation tasks, 3) applying learning in simulation to hardware performance, 4) learning under inconsistent, human-generated reward, and 5) combining the reinforcement learner with Probabilistic Roadmap Methods (PRM) to improve scalability. The goal of the paper is to demonstrate both the scalability of the BECCA reinforcement learning approach using different formulations of the state space and to show the approach in this paper operating on complex physical hardware.