Increasing Self-Adaptation in a Hybrid Decision-Making and Planning System with Reinforcement Learning

Abstract

Task-level decision-making and AI planning are used to control autonomous robots from a high-level, mission-oriented perspective. The dynamic selection of most suitable actions allows the system to adapt to changes in the environment as well as its own state. Nevertheless, decision-making and AI planning often require a priori definitions of capabilities, rules, decision models, or world knowledge. Due to the challenge of handling the uncertainty of robot applications in dynamic and uncontrolled environments such definitions or descriptions are always incomplete, hence the possible adaptation capabilities are limited. In this paper, we present how the self-adaptation of a robot planning and decision-making system can be improved by incorporating reinforcement learning. Particularly, we show our approach of integrating deep reinforcement learning into the ROS Hybrid Behaviour Planner (RHBP).