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Temporal Difference Learning of an Othello Evaluation Function for a Small Neural Network with Shared Weights
2007
2007 IEEE Symposium on Computational Intelligence and Games
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This paper presents an artificial neural network with shared weights, trained to play the game of Othello by selfplay with Temporal Difference Learning (TDL). ...
The network performs as well as the champion of the CEC 2006 Othello Evaluation Function Competition. The TDL-trained network contains only 67 unique weights compared to 2113 for the champion. ...
Hidden unit bias: 0.046755 Input-to-hidden weights: Hidden unit bias: 0.074982 ...
doi:10.1109/cig.2007.368101
dblp:conf/cig/Manning07
fatcat:dbrz4eommbcdregxpx3xo2glrq
A Study of Artificial Neural Network Architectures for Othello Evaluation Functions
2007
Transactions of the Japanese society for artificial intelligence
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of 20 different artificial neural network (ANN) architectures to learn othello game board evaluation functions. ...
keywords: artificial neural network, temporal difference learning, reinforcement learning, board games, othello Summary In this study, we use temporal difference learning (TDL) to investigate the ability ...
The evaluation function was an artificial neural network (ANN) using a straightforward board encoding and trained through temporal difference learning (TDL) [Sutton 88, Sutton 98 ]. ...
doi:10.1527/tjsai.22.461
fatcat:t4eospe4fvcdxbf3ttzqhb52bm
Coevolutionary Temporal Difference Learning for Othello
2009
2009 IEEE Symposium on Computational Intelligence and Games
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coevolution with temporal difference learning. ...
This paper presents Coevolutionary Temporal Difference Learning (CTDL), a novel way of hybridizing coevolutionary search with reinforcement learning that works by interlacing one-population competitive ...
of Poland. ...
doi:10.1109/cig.2009.5286486
dblp:conf/cig/SzubertJK09
fatcat:2byzeqgxzbb33ju7fhbekwlli4
Ensemble approaches in evolutionary game strategies: A case study in Othello
2008
2008 IEEE Symposium On Computational Intelligence and Games
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The ensemble approach is tested on the Othello game with a weight piece counter representation. ...
Additionally, the computational cost of an exhaustive search for the selective ensemble is reduced by introducing multi-stage evaluations. ...
Chong et al. use evolutionary algorithms to learn spatial neural networks as an evaluation function for board configuration of Othello [17] . ...
doi:10.1109/cig.2008.5035642
dblp:conf/cig/KimC08
fatcat:sabx4zunybbonhulzekav5hzhy
Learning to Play Othello with Deep Neural Networks
2018
IEEE Transactions on Games
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2017 pre-print arXiv:1711.06583v1 fatcat:3we5igk6dzb4rof67p3dgnvt2y
In this paper, we verify whether CNN-based move predictors prove effective for Othello, a game with significantly different characteristics, including a much smaller board size and complete lack of translational ...
The empirical evaluation confirms high capabilities of neural move predictors and suggests a strong correlation between prediction accuracy and playing strength. ...
The suite consists of players with board evaluation functions encoded by weighted piece counter and n-tuple networks, trained by different methods including hand-design, temporal difference learning, evolution ...
doi:10.1109/tg.2018.2799997
fatcat:vj77jdqn7nhvte2wp574yatvwq
Predicting expert moves in the game of Othello using fully convolutional neural networks
2017
Figshare
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The main result is that using a raw board state representation, an 11-layer convolutional neural network can be trained to achieve 57.4% prediction accuracy on a test set, surpassing previous state of ...
Careful feature engineering is an important factor of artificial intelligence for games. ...
Artificial neural networks for Othello Binkley et al. investigated different artificial neural network (ANN) architectures for Othello using temporal difference learning. ...
doi:10.6084/m9.figshare.5326573
fatcat:eo7zqx2oebcdpak5llymtfyvhy
Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
[article]
2020
arXiv
pre-print
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In self-play, Monte Carlo Tree Search is used to train a deep neural network, that is then used in tree searches. ...
A secondary result of our experiments concerns the choice of optimization goals, for which we also provide recommendations. ...
Chong et al. described the evolution of neural networks for learning to play Othello [25] . Thill [29] . ...
arXiv:2003.05988v1
fatcat:y7mtudj3q5anbesfnviwtxd3pq
A Coevolutionary Model for The Virus Game
2006
2006 IEEE Symposium on Computational Intelligence and Games
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In this paper, coevolution is used to evolve Artificial Neural Networks (ANN) which evaluate board positions of a two player zero-sum game (The Virus Game). ...
The coevolved neural networks play at a level that beats a group of strong hand-crafted AI players. ...
The weights of a deterministic evaluation function are evolved using a co-adapted GA with explicit fitness sharing. ...
doi:10.1109/cig.2006.311680
dblp:conf/cig/CowlingNH06
fatcat:ww4ulouaufdu7ljclyahnrpewe
Automatic Generation of Evaluation Features for Computer Game Players
2007
2007 IEEE Symposium on Computational Intelligence and Games
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Evaluation functions are usually constructed manually as a weighted linear combination of evaluation features that characterize game positions. ...
Accuracy of evaluation functions is one of the critical factors in computer game players. ...
Once game features are generated, they can be automatically weighted to form an evaluation function through a variety of successful methods, including those based on neural networks, temporal difference ...
doi:10.1109/cig.2007.368108
dblp:conf/cig/MiwaYC07
fatcat:u5caqv4mdrejxktrr4z7q7ehwq
Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping
[article]
2016
arXiv
pre-print
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With the aim to develop a strong 2048 playing program, we employ temporal difference learning with systematic n-tuple networks. ...
We show that this basic method can be significantly improved with temporal coherence learning, multi-stage function approximator with weight promotion, carousel shaping, and redundant encoding. ...
Acknowledgments The author thank Marcin Szubert for his comments to the manuscript and Adam Szczepański for implementing an efficient C++ version of the 2048 agent. ...
arXiv:1604.05085v3
fatcat:ykrkoiibondldmefeivq52itri
AlphaZero-Inspired General Board Game Learning and Playing
[article]
2022
arXiv
pre-print
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In this paper, we pick an important element of AlphaZero - the Monte Carlo Tree Search (MCTS) planning stage - and combine it with reinforcement learning (RL) agents. ...
Recently, the seminal algorithms AlphaGo and AlphaZero have started a new era in game learning and deep reinforcement learning. ...
Other function approximation networks (deep neural networks or other) could be used as well in AlphaZero-inspired reinforcement learning, but n-tuple networks have the advantage that they can be trained ...
arXiv:2204.13307v1
fatcat:v2u4b3pylnhknn3ros4nullxsm
Coevolutionary Temporal Difference Learning for small-board Go
2010
IEEE Congress on Evolutionary Computation
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In this paper we apply Coevolutionary Temporal Difference Learning (CTDL), a hybrid of coevolutionary search and reinforcement learning proposed in our former study, to evolve strategies for playing the ...
CTDL works by interlacing exploration of the search space provided by one-population competitive coevolution and exploitation by means of temporal difference learning. ...
ACKNOWLEDGMENTS This work was supported in part by Ministry of Science and Higher Education grant # N N519 3505 33. ...
doi:10.1109/cec.2010.5586054
dblp:conf/cec/KrawiecS10
fatcat:qi65gddgungrxbtbxu2nte3bj4
Evolving small-board Go players using coevolutionary temporal difference learning with archives
2011
International Journal of Applied Mathematics and Computer Science
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Evolving small-board Go players using coevolutionary temporal difference learning with archives We apply Coevolutionary Temporal Difference Learning (CTDL) to learn small-board Go strategies represented ...
Intra-game learning is driven by gradient-descent Temporal Difference Learning (TDL), a reinforcement learning method that updates the board evaluation function according to differences observed between ...
Acknowledgment This work has been supported by the Polish Ministry of Science and Higher Education under the grant no. N N519 441939. ...
doi:10.2478/v10006-011-0057-3
fatcat:uv6tkgqbbfaulblu3jv5da2yp4
Evolutionary computation and games
2006
IEEE Computational Intelligence Magazine
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Blondie24 utilizes an artificial neural network with 5,046 weights, which are evolved by an evolutionary strategy. ...
Natural evolution can be considered to be a game in which the rewards for an organism that plays a good game of life are the propagation of its genetic material to its successors and its continued survival ...
Acknowledgments We thank Thomas Runarsson for discussions related to this article. ...
doi:10.1109/mci.2006.1597057
fatcat:6o7yidxnirftvarbpx2fcxqh44
Mastering the game of Go with deep neural networks and tree search
2016
Nature
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These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. ...
We also introduce a new search algorithm that combines Monte-Carlo simulation with value and policy networks. ...
Acknowledgements We thank Fan Hui for agreeing to play against AlphaGo; Toby Manning for refereeing the match; Ostrovski for reviewing the paper; and the rest of the DeepMind team for their support, ideas ...
doi:10.1038/nature16961
pmid:26819042
fatcat:hhxixsirtjairjuwqivmi3gcga
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