Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain. This review mainly covers artificial-intelligence approaches to RL, from the viewpoint of the control engineer. We explain how approximate representations of the solution make RL feasible for problems with continuous states and control actions. Stability is a central concern in control, and we argue that while the control-theoretic RL subfield called adaptive dynamic programming is dedicated to it, stability of RL largely remains an open question. We also cover in detail the case where deep neural networks are used for approximation, leading to the field of deep RL, which has shown great success in recent years. With the control practitioner in mind, we outline opportunities and pitfalls of deep RL; and we close the survey with an outlook that -among other things -points out some avenues for bridging the gap between control and artificial-intelligence RL techniques.

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Cargo-bearing unmanned aerial vehicles (UAVs) have tremendous potential to assist humans by delivering food, medicine, and other supplies. For time-critical cargo delivery tasks, UAVs need to be able to quickly navigate their environments and deliver suspended payloads with bounded load displacement. As a constraint balancing task for joint UAV-suspended load system dynamics, this task poses a challenge. This article presents a reinforcement learning approach for aerial cargo delivery tasks in environments with static obstacles. We first learn a minimal residual oscillations task policy in obstacle-free environments using a specifically designed feature vector for value function approximation that allows generalization beyond the training domain. The method works in continuous state and discrete action spaces. Since planning for aerial cargo requires very large action space (over 10 6 actions) that is impractical for learning, we define formal conditions for a class of robotics problems where learning can occur in a simplified problem space and successfully transfer to a broader problem space. Exploiting these guarantees and relying on the discrete action space, we learn the swing-free policy in a subspace several orders of magnitude smaller, and later develop a method for swing-free trajectory planning along a path. As an extension to tasks in environments with static obstacles where the load displacement needs to be bounded throughout the trajectory, sampling-based motion planning generates collision-free paths. Next, a reinforcement learning agent transforms these paths into trajectories that maintain the bound on the load displacement while following the collision-free path in a timely manner. We verify the approach both in simulation and in experiments on a quadrotor with suspended load and verify the method's safety and feasibility through a demonstration where a quadrotor delivers an open container of liquid to a human subject. The contributions of this work are twofold. First, this article presents a solution to a challenging, and vital problem of planning a constraint-balancing task for an inherently unstable non-linear system in the presence of obstacles. Second, AI and robotics researchers can both benefit from the provided theoretical guarantees of system stability on a class of constraint-balancing tasks that occur in very large action spaces.

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