Learning to Prune Dominated Action Sequences in Online Black-Box Planning
By Yuu Jinnai, Alex S. Fukunaga. 2016
18 citations recorded by the source corpus.
Black-box domains where the successor states generated by applying an action are generated by a completely opaque simulator pose a challenge for domain-independent planning. The main computational bottleneck in search-based planning for such domains is the number of calls to the black-box simulation. We propose a method for significantly reducing the number of calls to the simulator by the search algorithm by detecting and pruning sequences of actions which are dominated by others. We apply our pruning method to Iterated Width and breadth-first search in domain-independent black-box planning for Atari 2600 games in the Arcade Learning Environment (ALE), adding our pruning method significantly improves upon the baseline algorithms.
Subfields: Artificial Intelligence, Reinforcement Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Marc G. Bellemare
- Marlos C. Machado
- Sergey Levine
- Michael H. Bowling
- Matthew J. Hausknecht
- Erik Talvitie
- Yuxi Li
- Pieter Abbeel
In Reinforcement Learning
- Marc G. Bellemare
- Marlos C. Machado
- Michael H. Bowling
- Matthew J. Hausknecht
- Sergey Levine
- Erik Talvitie
- Joel Veness
- Yuxi Li
Citations in the corpus
- Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (2017, methodology): “…remains rare compared to methods that learn policies or value functions (but see the papers by Bellemare, Naddaf, et al., 2013; Guo, Singh, Lee, Lewis, & Wang, 2014; Jinnai & Fukunaga, 2017; Lipovetzky et al., 2015; Shleyfman, Tuisov, & Domshlak, 2016, for published planning results in the ALE).”