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

Ricardo Quinteiro; Francisco S. Melo; Pedro A. Santos

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arXiv · 2106.15594

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

Ricardo Quinteiro·Francisco S. Melo·Pedro A. Santos

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Abstract

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.

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BibTeX RIS

Ricardo Quinteiro, Francisco S. Melo, Pedro A. Santos. 2021-06-29. Limited depth bandit-based strategy for Monte Carlo planning in continuous action spaces. https://arxiv.org/abs/2106.15594

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