title: Model-Based Reinforcement Learning for Infinite-Horizon Approximate Optimal Tracking.
abstract: This brief paper provides an approximate online adaptive solution to the infinite-horizon optimal tracking problem for control-affine continuous-time nonlinear systems with unknown drift dynamics. To relax the persistence of excitation condition, model-based reinforcement learning is implemented using a concurrent-learning-based system identifier to simulate experience by evaluating the Bellman error over unexplored areas of the state space. Tracking of the desired trajectory and convergence of the developed policy to a neighborhood of the optimal policy are established via Lyapunov-based stability analysis. Simulation results demonstrate the effectiveness of the developed technique.
authors: Rushikesh
authors: R
authors: Kamalapurkar
authors: Lindsey
authors: L
authors: Andrews
authors: Patrick
authors: P
authors: Walters
authors: Warren E
authors: WE
authors: Dixon