Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess

Author:

Bertram Timo1,Furnkranz Johannes1,Muller Martin2

Affiliation:

1. JKU Linz,Austria

2. University of Alberta,Canada

Publisher

IEEE

Reference18 articles.

1. Deep synoptic Monte-Carlo planning in reconnaissance blind chess;clark;Advances in neural information processing systems,2021

2. Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games Using Convolutional Networks

3. Deep reinforcement learning from self-play in imperfect-information games;heinrich;CoRR,2016

4. DREAM: Deep regret minimization with advantage baselines and model-free learning;steinberger;CoRR,2020

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1. Capitalizing on the Opponent's Uncertainty in Reconnaissance Blind Chess;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30

2. Voice Mate: Voice Controlled Chessboard for Visually Impaired;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28

3. Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets;Lecture Notes in Computer Science;2024

4. Chess Match Outcome Prediction via Sequential Data Analysis with Deep Learning;2023 International Conference on Sustainable Communication Networks and Application (ICSCNA);2023-11-15

5. Weighting Information Sets with Siamese Neural Networks in Reconnaissance Blind Chess;2023 IEEE Conference on Games (CoG);2023-08-21

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