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readme.txt
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The current release includes 4 trained models when the limited maximum bids are 2 to 5, and the dataset.
The dataset is saved in data_train.mat, cost_train.mat, data_validate.mat, cost_validate.mat, data_test.mat, cost_test.mat.
The dataset is generated by modifying the Code of Ho-Chun Yen, the author of 'Contract Bridge Bidding by Learning' http://www.csie.ntu.edu.tw/~htlin/paper/doc/wscpii15bridgebid.pdf
To run the cost-calculating code for the model, simply run the 'automatic_bridge_bidding_model.m' code in matlab.
The totalbid variable in automatic_bridge_bidding_model.m can be changed from 2 to 5, where a respective trained model will be loaded.
The release of the code is to allow comparison with automatic_bridge_bidding_with_deep_reinforcement_learning in the non-competing setting.
The model can be read by numpy and load into a python file if preferred.
Experiments for bidding against the current model can be achieved by bidding PASS for the current model when the cost of all possible bids are > 0.20.