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

Liam Schramm; Abdeslam Boularias

2024 ICML ICML 2024

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

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

🌉 Interdisciplinary Bridge - Reinforcement Learning and Robotics

Authors

Liam Schramm, Abdeslam Boularias

Topics

Reinforcement Learning > Methods > Policy Learning Robotics > Capabilities > Motion Planning Robotics > Capabilities > Navigation

Keywords

robot navigation monte carlo tree search count-based exploration rapidly-exploring random tree state-occupancy measure regularization volume-mcts

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