Robust and Efficient Planning using Adaptive Entropy Tree Search.
Year: 2021
Type: preprint
Abstract: In this paper, we present the Adaptive EntropyTree Search (ANTS) algorithm. ANTS builds on recent successes of maximum entropy planning while mitigating its arguably major drawback - sensitivity to the temperature setting. We endow ANTS with a mechanism, which adapts the temperature to match a given range of action selection entropy in the nodes of the planning tree. With this mechanism, the ANTS planner enjoys remarkable hyper-parameter robustness, achieves high scores on the Atari benchmark, and is a capable component of a planning-learning loop akin to AlphaZero. We believe that all these features make ANTS a compelling choice for a general planner for complex tasks.
Source: arXiv (Cornell University)
Authors: Piotr Kozakowski, Mikołaj Pacek, Piotr Miłoś
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3130703196