New RL method learns rewards without environment sampling

Researchers have developed a new method called Experience-Free Autonomous Reward Specification (EARS) for designing reward functions in reinforcement learning without requiring environment interaction. This approach uses a Large Language Model (LLM) to construct reward features from a task description and then learns feature weights from preferences over imagined trajectories. EARS has been evaluated on complex, long-horizon tasks including pandemic regulation, insulin administration, and autonomous vehicle control, demonstrating its effectiveness in creating reward functions more aligned with desired outcomes compared to other interaction-free methods. AI

IMPACT Enables reward function design in settings where environment interaction is costly or infeasible, potentially accelerating RL deployment.

RANK_REASON Academic paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Experience-Free Autonomous Reward Specification
• Hugging Face
• Large Language Model
• reinforcement learning
• Stephane Hatgis-Kessell

• paper
• other

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