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Coachable agents for interactive gameplay.
Roberto Capobianco, Harm van Seijen, Nolan D. Bard, Neil Burch, Fatima
Davelouis, Josh Davidson, Alisa Devlic, Yunshu Du, Ishan Durugkar, Siddhant Gangapurwala,
Daniel Hernandez, G. Zacharias Holland, Sahil Jain, Kenta Kawamoto, Raksha Kumaraswamy, Patrick
MacAlpine, Dustin R. Morrill, Declan Oller, Francesco Riccio, Akanksha Saran, Craig Sherstan, Kaushik Subramanian, Thomas
J. Walsh, Samuel Barrett, Kizza N. Frisbee, Mady Govil, Johannes Günther,
Varun R. Kompella, James A. MacGlashan, Maxwell Svetlik, Michael D. Thomure, Jaden B. Travnik, Kevin Waugh, Elahe Aghapour,
Florian Fuchs, Andreanne Lemay, Shruti Mishra, Takuma Seno, Peter Stone, Michael
Spranger, and Peter R. Wurman.
Technical Report arXiv e-Prints 2607.00642, arXiv,
2026.
arXiv version
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation models. Through trial-and-error, these AI systems typically learn one, near-optimal behavior to solve their tasks. However, there are many use cases in which one would like to assert some level of control, preferably in real time, over how the task is solved. We refer to these modifications of a core task as styles. We combine universal value function approximators (UVFAs) with carefully selected training scenarios, learning algorithms, and data augmentation to create a framework for coaching agents that exhibit styles in complex domains. We demonstrate the framework's application in the AAA video games Horizon Forbidden West and Gran Turismo, and in an open-source humanoid test domain. Despite the different nature of the domains -- car racing, stylized game combat, and humanoid walking -- each agent shows strong coherence to the style requests while still satisfying the main task in its domain. Importantly, the techniques outlined in this paper allow an end user to choose the final behavior at run time, giving them flexible control over the final executed performance.
@Techreport{roberto_capobianco_2026,
author = {Roberto Capobianco and Harm van Seijen and Nolan D. Bard and Neil Burch and Fatima Davelouis and Josh Davidson and Alisa Devlic and Yunshu Du and Ishan Durugkar and Siddhant Gangapurwala and Daniel Hernandez and G. Zacharias Holland and Sahil Jain and Kenta Kawamoto and Raksha Kumaraswamy and Patrick MacAlpine and Dustin R. Morrill and Declan Oller and Francesco Riccio and Akanksha Saran and Craig Sherstan and Kaushik Subramanian and Thomas J. Walsh and Samuel Barrett and Kizza N. Frisbee and Mady Govil and Johannes Günther and Varun R. Kompella and James A. MacGlashan and Maxwell Svetlik and Michael D. Thomure and Jaden B. Travnik and Kevin Waugh and Elahe Aghapour and Florian Fuchs and Andreanne Lemay and Shruti Mishra and Takuma Seno and Peter Stone and Michael Spranger and Peter R. Wurman},
title = {Coachable agents for interactive gameplay},
institution = "arXiv",
number = "arXiv e-Prints 2607.00642",
year = {2026},
abstract = {Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation models. Through trial-and-error, these AI systems typically learn one, near-optimal behavior to solve their tasks. However, there are many use cases in which one would like to assert some level of control, preferably in real time, over how the task is solved. We refer to these modifications of a core task as styles. We combine universal value function approximators (UVFAs) with carefully selected training scenarios, learning algorithms, and data augmentation to create a framework for coaching agents that exhibit styles in complex domains. We demonstrate the framework's application in the AAA video games Horizon Forbidden West and Gran Turismo, and in an open-source humanoid test domain. Despite the different nature of the domains -- car racing, stylized game combat, and humanoid walking -- each agent shows strong coherence to the style requests while still satisfying the main task in its domain. Importantly, the techniques outlined in this paper allow an end user to choose the final behavior at run time, giving them flexible control over the final executed performance.},
wwwnote={<a href="https://arxiv.org/abs/2607.00642">arXiv version</a>},
}
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