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Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost.
The implementation shortfall: Paper vs. reality
A. F. Perold · 1988
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Value under liquidation
Robert Almgren and Neil Chriss · 1999
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Optimal execution of portfolio transactions
Robert Almgren and Neil Chriss · 2001
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Aggressive orders and the resiliency of a limit order market
Hans Degryse, Frank De Jong, Maarten Van Ravenswaaij, and Gunther Wuyts · 2005
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Reinforcement learning for optimized trade execution
Yuriy Nevmyvaka, Yi Feng, and Michael Kearns · 2006
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Reinforcement learning in environments with independent delayed-sense dynamics
Masoud Shahamiri · 2008
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Further analysis of the speed of response to large trades in interest rate futures
James Richard Cummings and Alex Frino · 2010
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Optimal portfolio liquidation with limit orders
Olivier Guéant, Charles-Albert Lehalle, and Joaquin Fernandez-Tapia · 2012
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Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model
Mohammad Gheshlaghi Azar, Rémi Munos, and Hilbert J Kappen · 2013
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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Liquidity dynamics in an electronic open limit order book: An event study approach
Peter Gomber, Uwe Schweickert, and Erik Theissen · 2015
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Optimal execution of limit and market orders with trade director, speed limiter, and fill uncertainty
Brian Bulthuis, Julio Concha, Tim Leung, and Brian Ward · 2017
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Neural adaptive video streaming with pensieve
Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh · 2017
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Discovering and removing exogenous state variables and rewards for reinforcement learning
Thomas Dietterich, George Trimponias, and Zhitang Chen · 2018
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Variance reduction for reinforcement learning in input-driven environments
Hongzi Mao, Shaileshh Bojja Venkatakrishnan, Malte Schwarzkopf, and Mohammad Alizadeh · 2018
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Double deep Q-learning for optimal execution
Brian Ning, Franco Ho Ting Lin, and Sebastian Jaimungal · 2018
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Assessing generalization in deep reinforcement learning
Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, and Dawn Song · 2018
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Provably efficient RL with rich observations via latent state decoding
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, and John Langford · 2019
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Observational overfitting in reinforcement learning
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, and Behnam Neyshabur · 2019
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Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Chris Hesse, Jacob Hilton, and John Schulman · 2020
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A deep reinforcement learning framework for optimal trade execution
Siyu Lin and Peter A Beling · 2020
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Auxiliary-task based deep reinforcement learning for participant selection problem in mobile crowdsourcing
Wei Shen, Xiaonan He, Chuheng Zhang, Qiang Ni, Wanchun Dou, and Yan Wang · 2020
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A dissection of overfitting and generalization in continuous reinforcement learning
Amy Zhang, Nicolas Ballas, and Joelle Pineau · 2018
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A study on overfitting in deep reinforcement learning
Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio · 2018
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Reinforcement Learning: Theory and Algorithms
Alekh Agarwal, Nan Jiang, Sham M Kakade, and Wen Sun · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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ABIDES: Towards high-fidelity market simulation for AI research
David Byrd, Maria Hybinette, and Tucker Hybinette Balch · 2019
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Quantifying generalization in reinforcement learning
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, and John Schulman · 2019
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Deep execution-value and policy based reinforcement learning for trading and beating market benchmarks
Kevin Dabérius, Elvin Granat, and Patrik Karlsson · 2019
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Get real: Realism metrics for robust limit order book market simulations
Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, and Tucker Balch · 2020
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Improving generalization in reinforcement learning with mixture regularization
Kaixin Wang, Bingyi Kang, Jie Shao, and Jiashi Feng · 2020
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Universal trading for order execution with oracle policy distillation
Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, and Tie-Yan Liu · 2021
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A survey of generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2021
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An end-to-end optimal trade execution framework based on proximal policy optimization
Siyu Lin and Peter A Beling · 2021
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A deep Q-network for the beer game: Deep reinforcement learning for inventory optimization
Afshin Oroojlooyjadid, MohammadReza Nazari, Lawrence V Snyder, and Martin Takáč · 2022
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