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

• URL: http://arxiv.org/abs/2106.15594v1
• Date: Tue, 29 Jun 2021 17:30:01 GMT
• Title: Limited depth bandit-based strategy for Monte Carlo planning in continuous action spaces
• Authors: Ricardo Quinteiro, Francisco S. Melo, Pedro A. Santos
• Abstract summary: We propose LD-HOO, a limited depth variant of the hierarchical optimization (HOO) algorithm. We show that 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.
• Score: 4.1208902102156015
• License: http://creativecommons.org/licenses/by/4.0/
• 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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