1 citations · 1 across the 2 of their papers we have counts for
4 papers
Extreme Value Monte Carlo Tree Search for Classical Planning
Masataro Asai, Stephen Wissow
Despite being successful in board games and reinforcement learning (RL), Monte Carlo Tree Search (MCTS) combined with Multi Armed Bandits (MABs) has seen limited success in domain-…
Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning
Stephen Wissow, Masataro Asai
Balancing exploration and exploitation has been an important problem in both game tree search and automated planning. However, while the problem has been extensively analyzed withi…
Bilevel MCTS for Amortized O(1) Node Selection in Classical Planning
Masataro Asai
We study an efficient implementation of Multi-Armed Bandit (MAB)-based Monte-Carlo Tree Search (MCTS) for classical planning. One weakness of MCTS is that it spends a significant t…
On Using Admissible Bounds for Learning Forward Search Heuristics
Carlos Núñez-Molina, Masataro Asai, Pablo Mesejo +1
In recent years, there has been growing interest in utilizing modern machine learning techniques to learn heuristic functions for forward search algorithms. Despite this, there has…