How do we deploy RL safely in real-world systems?
Safe RL requires careful reward design.
How It Works:
Define clear reward functions, implement safety constraints (e.g., shields), and run extensive simulations before any real-world rollout.
Key Benefits:
- Controlled exploration: Prevents unsafe behaviors.
- Scalable learning: Transfer policies from simulation to production.
- Continuous improvement: Agents adapt from live feedback.
Real-World Use Cases:
- Autonomous driving: Train in simulated lanes before road testing.
- Energy management: Optimize HVAC systems with safety limits.
FAQs
What are reward hacks?
How test RL agents?
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