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We explore the reinforcement learning approach to designing controllers byextensively discussing the case of a quadcopter attitude controller. We provideall details allowing to reproduce our approach, starting with a model of thedynamics of a crazyflie 2.0 under various nominal and non-nominal conditions,including partial motor failures and wind gusts. We develop a robust form of asignal temporal logic to quantitatively evaluate the vehicle's behavior andmeasure the performance of controllers. The paper thoroughly describes thechoices in training algorithms, neural net architecture, hyperparameters,observation space in view of the different performance metrics we haveintroduced. We discuss the robustness of the obtained controllers, both topartial loss of power for one rotor and to wind gusts and finish by drawingconclusions on practical controller design by reinforcement learning.

Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts