NODEC: Neural ODE For Optimal Control of Unknown Dynamical Systems

Controlling complex dynamical systems is generally associated with minimizing certain control objectives with known dynamics under the variational calculus framework. For systems with unknown dynamics, an additional step of dynamics modeling is required. However, any inaccuracy in dynamics modeling will lead to sub-optimality in the resulting control function. Another set of approaches for controlling unknown dynamical systems - reinforcement learning, folds the dynamics modeling into controller training via value function approximation or policy gradient through extensively interacting with the environment, but it suffers from low data efficiency. To address these, we introduce NODEC, a novel framework for controlling unknown dynamical systems, which combines dynamics modelling and controller training using a coupled neural ODE model. Through an intriguing interplay between the two coupled neural networks, NODEC learns system dynamics as well as optimal controls that guides the unknown dynamical system towards target states. Our experiments demonstrate the effectiveness and data efficiency of NODEC for learning optimal control of unknown dynamical systems.