Coachable agents for interactive gameplay
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 ...
| Authors | Roberto Capobianco, Harm van Seijen, Nolan D. Bard et al. |
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| Primary category | cs.AI |
| Published | 1 July 2026 |
| Last updated at source | 10 August 2026 |
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