mlsanityData to 14 Oct 2026

Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search

arXiv 2104.00698 ↗First posted 1 Apr 2021cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

We present AlphaChute: a state-of-the-art algorithm that achieves superhuman performance in the ancient game of Chutes and Ladders. We prove that our algorithm converges to the Nash equilibrium in constant time, and therefore is -- to the best of our knowledge -- the first such formal solution to this game. Surprisingly, despite all this, our implementation of AlphaChute remains relatively straightforward due to domain-specific adaptations. We provide the source code for AlphaChute here in our Appendix.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 242 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

35 of 242 references.

References · 35

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2016Layer Normalizationcited by 5,669
  4. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  5. 2019Similarity of Neural Network Representations Revisitedcited by 1,755
  6. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
  7. 2019When Does Label Smoothing Help?cited by 988
  8. 2017Dynamic Routing Between Capsulescited by 689
  9. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
  10. 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 464
  11. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 336
  12. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 319
23 more
  1. 2017Distilling a Neural Network Into a Soft Decision Treecited by 236
  2. 2016Attend, Infer, Repeat: Fast Scene Understanding with Generative Modelscited by 209
  3. 2019CvxNet: Learnable Convex Decompositioncited by 174
  4. 2016Using Fast Weights to Attend to the Recent Pastcited by 170
  5. 2019NASA: Neural Articulated Shape Approximationcited by 149
  6. 2018Large scale distributed neural network training through online distillationcited by 146
  7. 2015A Simple Way to Initialize Recurrent Networks of Rectified Linear Unitscited by 130
  8. 2018Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architecturescited by 101
  9. 2019Analyzing and Improving Representations with the Soft Nearest Neighbor Losscited by 95
  10. 2019Stacked Capsule Autoencoderscited by 82
  11. 2017Who Said What: Modeling Individual Labelers Improves Classificationcited by 77
  12. 2014Grammar as a Foreign Languagecited by 70
  13. 2020Imputer: Sequence Modelling via Imputation and Dynamic Programmingcited by 66
  14. 2019Learning Sparse Networks Using Targeted Dropoutcited by 40
  15. 2019Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructionscited by 30
  16. 2020Canonical Capsules: Self-Supervised Capsules in Canonical Posecited by 30
  17. 2020Unsupervised part representation by Flow Capsulescited by 25
  18. 2020Subclass Distillationcited by 23
  19. 2020Teaching with Commentariescited by 16
  20. 2013Modeling Documents with Deep Boltzmann Machinescited by 10
  21. 2018DARCCC: Detecting Adversaries by Reconstruction from Class Conditional Capsulescited by 9
  22. 2019Cerberus: A Multi-headed Derenderercited by 4
  23. 2013Discovering Multiple Constraints that are Frequently Approximately Satisfiedcited by 2

Built on by

Not cited yet.