# A Tractable Algorithm For Finite-Horizon Continuous Reinforcement Learning

arXiv: https://arxiv.org/abs/1906.11245 · HTML: https://mlsanity.com/paper/1906.11245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

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

- [Sublinear Regret for Learning POMDPs](https://mlsanity.com/paper/2107.03635/) (2021)

## Abstract (arXiv)

We consider the finite horizon continuous reinforcement learning problem. Our contribution is three-fold. First,we give a tractable algorithm based on optimistic value iteration for the problem. Next,we give a lower bound on regret of order Omega(T2/3) for any algorithm discretizes the state space, improving the previous regret bound of Omega(T1/2) of Ortner and Ryabko citecontrl for the same problem. Next,under the assumption that the rewards and transitions are H"older Continuous we show that the upper bound on the discretization error is const.Ln-αT. Finally,we give some simple experiments to validate our propositions.