Continuous action reinforcement learning for control-affine systems with unknown dynamics

Control of nonlinear systems is challenging in realtime. Decision making, performed many times per second, must ensure system safety. Designing input to perform a task often involves solving a nonlinear system of differential equations, which is a computationally intensive, if not intractable problem. This article proposes sampling-based task learning for control-affine nonlinear systems through the combined learning of both state and action-value functions in a model-free approximate value iteration setting with continuous inputs. A quadratic negative definite state-value function implies the existence of a unique maximum of the action-value function at any state. This allows the replacement of the standard greedy policy with a computationally efficient policy approximation that guarantees progression to a goal state without knowledge of the system dynamics. The policy approximation is consistent, i.e., it does not depend on the action samples used to calculate it. This method is appropriate for mechanical systems with high-dimensional input spaces and unknown dynamics performing Constraint-Balancing Tasks. We verify it both in simulation and experimentally for an Unmanned Aerial Vehicles (UAVs) carrying a suspended load, and in simulation, for the rendezvous of heterogeneous robots.