"Self-Play and Using an Expert to Learn to Play Backgammon with Temporal Difference Learning"
written by Marco A. Wiering,
published by Journal of Intelligent Learning Systems and Applications, Vol.2 No.2, 2010
has been cited by the following article(s):
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[1] Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
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[2] Warm-Start AlphaZero Self-Play Search Enhancements
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[3] Reinforcement Learning agents playing Hearthstone: The influence of state description when learning in a large, partially observable state space
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[4] Applying Reinforcement Learning with Monte Carlo Tree Search to The Game of Draughts
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[5] Policy or Value? Loss Function and Playing Strength in AlphaZero-like Self-play
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[6] Hyper-Parameter Sweep on AlphaZero General
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[7] Alternative loss functions in alphazero-like self-play
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[8] Development of a Computer Player for Seejeh (AKA Seega, Siga, Kharbga) Board Game with Deep Reinforcement Learning
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[9] A Multi-agent Design of a Computer Player for Nine Men's Morris Board Game using Deep Reinforcement Learning
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[10] Competitive Evolution Multi-Agent Deep Reinforcement Learning
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[11] Hyper Dice Backgammon of Finite Size
2018
[12] Investigating Deep Learning and Self Play in Artificial Intelligent Games
International Journal of Innovative Science, Engineering & Technology, 2018
[13] A bright future for financial agent-based models
2018
[14] Temporal Difference Learning for the Game Tic-Tac-Toe 3D: Applying Structure to Neural Networks
Computational Intelligence, 2015 IEEE Symposium Series on, 2015
[15] Decision making in multiplayer environments: application in backgammon variants
2015
[16] Discussion
Design of Experiments for Reinforcement Learning, 2015
[17] Reinforcement Learning
Design of Experiments for Reinforcement Learning.Springer, 2015
[18] The Mountain Car Problem
Design of Experiments for Reinforcement Learning.Springer, 2015
[19] The Truck Backer-upper Problem
Design of Experiments for Reinforcement Learning.Springer, 2015
[20] Self-Optimizing Evaluation Function for Chinese-Chess.
International Journal of Hybrid Information Technology, 2014
[21] Design of experiments for reinforcement learning
2014
[22] On Constructing Static Evaluation Function using Temporal Difference Learning
2013
[23] A Novel Technique to Find Outliers in Mixed Attribute Datasets
Computer Engineering and Applications Journal, 2013
[24] Reinforcement learning in the game of Othello: Learning against a fixed opponent and learning from self-play
Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2013 IEEE Symposium on. IEEE, 2013
[25] Reinforcement Learning with Neural Networks: Tricks of the Trade
Advances in Intelligent Signal Processing and Data Mining. Springer Berlin Heidelberg, 2013
[26] Improving Temporal Difference Learning Performance in Backgammon Variants
Advances in Computer Games, 2012
[27] Conclusions, Future Directions and Outlook
Reinforcement Learning, 2012
[28] On the Design and Training of Bots to Play Backgammon Variants
Artificial Intelligence Applications and Innovations. Springer Berlin Heidelberg, 2012
[29] Self-teaching adaptive dynamic programming for Gomoku
Neurocomputing, 2012
[30] Reinforcement learning in games
Reinforcement Learning, 2012
[31] Reinforcement learning and the effects of parameter settings in the game of Chung Toi
Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on. IEEE, 2011
[32] A brief tutorial on reinforcement learning: The game of Chung Toi.
ESANN, 2011
[33] Special Section on Reinforcement Learning and Approximate Dynamic Programming.
2010
[34] Editorial: Special Section on Reinforcement Learning and Approximate Dynamic Programming
Journal of Intelligent Learning Systems and Applications, 2010