Risk and reward are fundamental concepts in the cooperative control of unmanned systems. This paper focuses on a constructive relationship between a cooperative planner and a learner in order to mitigate the learning risk while boosting the asymptotic performance and safety of agent behavior. Our framework is an instance of the intelligent cooperative control architecture (iCCA) where a learner (Natural actor-critic, Sarsa) initially follows a "safe" policy generated by a cooper- ative planner (consensus-based bundle algorithm). The learner incrementally improves this baseline policy through interaction, while avoiding behaviors believed to be "risky". This paper extends previous work toward the coupling of learning and cooperative control strategies in real-time stochastic domains in two ways: (1) the risk analysis module supports stochastic risk models, and (2) learning schemes that do not store the policy as a separate entity are integrated with the cooperative planner extending the applicability of iCCA framework. The performance of the resulting approaches are demonstrated through simulation of limited fuel UAVs in a stochastic task assignment problem. Results show an 8% reduction in risk, while improving the performance up to 30%. I. INTRODUCTION

Published in 2011