# Model-based reinforcement learning for infinite-horizon approximate optimal tracking

arXiv: https://arxiv.org/abs/1506.00685 · HTML: https://mlsanity.com/paper/1506.00685/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2015.

## Built on by (most cited first, 2 of 2)

- [Online inverse reinforcement learning with limited data](https://mlsanity.com/paper/2008.08972/) (2020)
- [Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control](https://mlsanity.com/paper/2201.13331/) (2022)

## Abstract (arXiv)

This 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. Model-based reinforcement learning is used 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.