# Model Based Reinforcement Learning with Final Time Horizon Optimization

arXiv: https://arxiv.org/abs/1509.01186 · HTML: https://mlsanity.com/paper/1509.01186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [Second-Order Neural ODE Optimizer](https://mlsanity.com/paper/2109.14158/) (2021)

## Abstract (arXiv)

We present one of the first algorithms on model based reinforcement learning and trajectory optimization with free final time horizon. Grounded on the optimal control theory and Dynamic Programming, we derive a set of backward differential equations that propagate the value function and provide the optimal control policy and the optimal time horizon. The resulting policy generalizes previous results in model based trajectory optimization. Our analysis shows that the proposed algorithm recovers the theoretical optimal solution on linear low dimensional problem. Finally we provide application results on nonlinear systems.