Improving Control Performance of Unmanned Aerial Vehicles through Shared Experience

This work proposes a novel approach for improving the control performance of Unmanned Aerial Vehicles (UAVs) through cooperative reinforcement learning. By sharing their experience, it is shown that multiple UAVs can work together to converge on a set of optimal Model Predictive Control (MPC) parameters faster than when working on their own. In order to benefit from this shared experience, the UAVs must coordinate their learning strategies. Here, we proposed a Leader-Follower approach, whereby the Leader ensures all trials are drawn from the same distribution and contribute to a common payoff game of Learning Automata. Experimental results show that this approach results in faster learning without any loss of performance.