Temporal difference learning applied to game playing and the results of application to Shogi

By D. Beal, Martin C. Smith. 2001

23 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Economics and Econometrics, 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. Sergey Levine
2. Pieter Abbeel
3. Yuxi Li
4. Yoshua Bengio
5. Timothy P. Lillicrap
6. David Silver
7. Chelsea Finn
8. Ruslan Salakhutdinov

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Sergey Levine
3. Trevor Darrell
4. Dacheng Tao
5. Jitendra Malik
6. Abhinav Kumar Gupta
7. Andrea Vedaldi
8. Andrew Zisserman

In Economics and Econometrics

1. Matthew Lai
2. Arthur Guez
3. Tomoyuki Kaneko
4. Timothy P. Lillicrap
5. David Silver
6. Marc Lanctot
7. Thore Graepel
8. Demis Hassabis

In Reinforcement Learning

1. Sergey Levine
2. Pieter Abbeel
3. Timothy P. Lillicrap
4. Yuxi Li
5. David Silver
6. Jimmy Ba
7. Shimon Whiteson
8. R´emi Munos

Citations in the corpus

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017, background): “Beal and Smith applied temporal-difference learning to estimate the piece values in chess (7) and shogi (8), starting from random values and learning solely by self-play.”
• Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes (2011, unknown): “, [3]).”
• Large-Scale Optimization for Evaluation Functions with Minimax Search (2014, background): “The studies published so far have adjusted only piece values or a small part of the feature weights in the evaluation functions (Beal & Smith, 2001; Ugajin & Kotani, 2010).”