Solving the Rubik’s cube with deep reinforcement learning and search

By Forest Agostinelli, Stephen Marcus McAleer, Alexander Shmakov, Pierre Baldi. 2019

210 citations recorded by the source corpus.

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

1. Emma Strubell
2. Ananya Ganesh
3. Andrew McCallum
4. Percy Liang
5. Hinrich Schütze
6. Burr Settles
7. Noah A. Smith
8. Iryna Gurevych

In Reinforcement Learning

1. Andrew McCallum
2. Emma Strubell

Citations in the corpus

• Energy and Policy Considerations for Modern Deep Learning Research (2020, background): “2019; Aharoni, Johnson, and Firat 2019), computer vision (Chollet 2017; Brock, Donahue, and Simonyan 2019) and robotics (Agostinelli et al. 2019).”
• Automated curricula through setter-solver interactions (2019, unknown): “For example, Agostinelli et al. (2019) trained a Rubik’s cube solver on problems sampled uniformly between 1 and K moves from the solved state.”