Remote Action Generation: Remote Control with Minimal Communication
📰 ArXiv cs.AI
Learn to generate remote actions with minimal communication using a novel controller-actor framework
Action Steps
- Implement a controller-actor framework to learn optimal policies from observed rewards
- Use the framework to communicate action guidance to actors over a communication-constrained channel
- Optimize the communication process to achieve rate-efficient interaction
- Test the framework with large or continuous action spaces to evaluate its effectiveness
- Apply the remote action generation approach to real-world remote control scenarios, such as robotics or autonomous vehicles
Who Needs to Know This
Researchers and engineers working on remote control systems, particularly those with limited communication channels, can benefit from this approach to optimize their systems
Key Insight
💡 Minimal communication can be achieved in remote control systems using a controller-actor framework that learns optimal policies from observed rewards
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🚀 Remote Action Generation: Learn to control with minimal communication! 📱💻
Key Takeaways
Learn to generate remote actions with minimal communication using a novel controller-actor framework
Full Article
Title: Remote Action Generation: Remote Control with Minimal Communication
Abstract:
arXiv:2605.01833v1 Announce Type: cross Abstract: We address the challenge of remote control where one or more actors, lacking direct reward access, are steered by a controller over a communication-constrained channel. The controller learns an optimal policy from observed rewards and communicates action guidance to the actors, which becomes demanding for large or continuous action spaces. To achieve rate-efficient communication throughout this interactive learning and control process, we introdu
Abstract:
arXiv:2605.01833v1 Announce Type: cross Abstract: We address the challenge of remote control where one or more actors, lacking direct reward access, are steered by a controller over a communication-constrained channel. The controller learns an optimal policy from observed rewards and communicates action guidance to the actors, which becomes demanding for large or continuous action spaces. To achieve rate-efficient communication throughout this interactive learning and control process, we introdu
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