Learning to classify and imitate trading agents in continuous double auction markets

Author:

Mahfouz Mahmoud1,Balch Tucker2,Veloso Manuela2,Mandic Danilo1

Affiliation:

1. Imperial College London, London, United Kingdom

2. J.P. Morgan AI Research

Publisher

ACM

Reference41 articles.

1. Stefano Vittorino Albrecht , Jacob William Crandall , and Subramanian Ramamoorthy . 2015 . E-HBA: Using action policies for expert advice and agent typification . In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence. Stefano Vittorino Albrecht, Jacob William Crandall, and Subramanian Ramamoorthy. 2015. E-HBA: Using action policies for expert advice and agent typification. In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence.

2. Autonomous agents modelling other agents: A comprehensive survey and open problems

3. David Balduzzi , Sebastien Racaniere , James Martens , Jakob Foerster , Karl Tuyls , and Thore Graepel . 2018. The mechanics of n-player differentiable games. arXiv preprint arXiv:1802.05642 ( 2018 ). David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, and Thore Graepel. 2018. The mechanics of n-player differentiable games. arXiv preprint arXiv:1802.05642 (2018).

4. James Bergstra , Daniel Yamins , and David Cox . 2013 . Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures . In International conference on machine learning. 115--123 . James Bergstra, Daniel Yamins, and David Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning. 115--123.

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1. Deep Reinforcement Learning-Based Pricing Strategy in Double-Auction Market for Edge Computing Resource Allocation;2022 International Conference on Networks, Communications and Information Technology (CNCIT);2022-06

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