Coachable agents for interactive gameplay

📰 ArXiv cs.AI

Learn to create coachable agents for interactive gameplay using reinforcement learning and real-time control

advanced Published 2 Jul 2026

Action Steps

Implement reinforcement learning algorithms to train AI agents
Use real-time feedback to coach agents and modify their behavior
Configure agents to respond to user input and adapt to changing game environments
Test coachable agents in various gameplay scenarios to evaluate their performance
Apply coachable agents to different game types, such as strategy or puzzle games

Who Needs to Know This

AI researchers and game developers can benefit from this technique to create more interactive and controllable game agents

Key Insight

💡 Coachable agents can be created using reinforcement learning and real-time control, enabling more interactive and controllable gameplay experiences

Key Takeaways

Learn to create coachable agents for interactive gameplay using reinforcement learning and real-time control

Full Article

Title: Coachable agents for interactive gameplay

Abstract:
arXiv:2607.00642v1 Announce Type: new 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 t

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