Unmanned Aerial Vehicle Angular Velocity Control via Reinforcement Learning in Dimension Reduced Search Spaces

Search space dimension reduction strategies are studied for reinforcement learning based angular velocity control of multirotor unmanned aerial vehicles. Reinforcement learning approximates the value function iteratively over the state-action space, which is 6-dimensional in the case of multirotor angular velocity control. An inverse-dynamics approach is applied to reduce the 6-dimensional state-action space to a 3-dimensional state-only search space while estimating the uncertain model parameters. The search space dimension is further reduced when the state variables are only allowed to vary following either a motion camouflage strategy or a hyperbolic tangent path. Simulation results show that the modified reinforcement learning algorithms can be implemented in real time for multirotor angular velocity control.