Coachable agents for interactive gameplay
Quick summary
arXiv:2607.00642v2 Announce Type: replace 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 (
Key takeaways
- arXiv:2607.00642v2 Announce Type: replace 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.
Why it matters
This development shows AI moving deeper into everyday software. Productivity potential should be weighed against price, data permissions, exportability and the preservation of human control.
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