# Limited depth bandit-based strategy for Monte Carlo planning in continuous action spaces

arXiv: https://arxiv.org/abs/2106.15594 · HTML: https://mlsanity.com/paper/2106.15594/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 29 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis](https://mlsanity.com/paper/2006.04672/) (2020)

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

- [Continuous Monte Carlo Graph Search](https://mlsanity.com/paper/2210.01426/) (2022)

## Abstract (arXiv)

This paper addresses the problem of optimal control using search trees. We start by considering multi-armed bandit problems with continuous action spaces and propose LD-HOO, a limited depth variant of the hierarchical optimistic optimization (HOO) algorithm. We provide a regret analysis for LD-HOO and show that, asymptotically, our algorithm exhibits the same cumulative regret as the original HOO while being faster and more memory efficient. We then propose a Monte Carlo tree search algorithm based on LD-HOO for optimal control problems and illustrate the resulting approach's application in several optimal control problems.