Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization

Autor:	Schramm, Liam, Boularias, Abdeslam
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	Monte Carlo tree search (MCTS) has been successful in a variety of domains, but faces challenges with long-horizon exploration when compared to sampling-based motion planning algorithms like Rapidly-Exploring Random Trees. To address these limitations of MCTS, we derive a tree search algorithm based on policy optimization with state occupancy measure regularization, which we call {\it Volume-MCTS}. We show that count-based exploration and sampling-based motion planning can be derived as approximate solutions to this state occupancy measure regularized objective. We test our method on several robot navigation problems, and find that Volume-MCTS outperforms AlphaZero and displays significantly better long-horizon exploration properties. Comment: To be published in ICML 2024 Conference Proceedings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.05511 Zobrazit plný text záznamu View this record from Arxiv