[2607.00642] Coachable agents for interactive gameplay Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search --> Computer Science > Artificial Intelligence arXiv:2607.00642 (cs) [Submitted on 1 Jul 2026 ( v1 ), last revised 10 Aug 2026 (this version, v2)] Title: Coachable agents for interactive gameplay Authors: Roberto Capobianco (1), Harm van Seijen (2), Nolan D. Bard (2), Neil Burch (2), Fatima Davelouis (2), Josh Davidson (2), Alisa Devlic (1), Yunshu Du (2), Ishan Durugkar (2), Siddhant Gangapurwala (2), Daniel Hernandez (2), G. Zacharias Holland (2), Sahil Jain (2), Kenta Kawamoto (3), Raksha Kumaraswamy (2), Patrick MacAlpine (2), Dustin R. Morrill (2), Declan Oller (2), Francesco Riccio (1), Akanksha Saran (2), Craig Sherstan (3), Kaushik Subramanian (1), Thomas J. Walsh (2), Samuel Barrett (2), Kizza N. Frisbee (2), Mady Govil (2), Johannes Günther (2), Varun R. Kompella (2), James A. MacGlashan (2), Maxwell Svetlik (2), Michael D. Thomure (2), Jaden B. Travnik (2), Kevin Waugh (2), Elahe Aghapour (2), Florian Fuchs (1), Andreanne Lemay (2), Shruti Mishra (1), Takuma Seno (3), Peter Stone (2), Michael Spranger (3), Peter R. Wurman (2) ((1) Sony AI, Zurich, Switzerland, (2) Sony AI, North America, various locations, (3) Sony AI, Tokyo, Japan) View a PDF of the paper titled Coachable agents for interactive gameplay, by Roberto Capobianco (1) and 48 other authors View PDF HTML (experimental) 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. Comments: Source code: this https URL - Videos: this https URL Subjects: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG) Cite as: arXiv:2607.00642 [cs.AI] (or arXiv:2607.00642v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.00642 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Harm van Seijen [ view email ] [v1] Wed, 1 Jul 2026 08:57:25 UTC (8,673 KB) [v2] Mon, 10 Aug 2026 18:34:38 UTC (8,673 KB) Full-text links: Access Paper: View a PDF of the paper titled Coachable agents for interactive gameplay, by Roberto Capobianco (1) and 48 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-07 Change to brows