Beyond Scripted Search: Sample-Efficient Reward Discovery via Agentic Black-box Optimization
Designing dense reward functions for low-level reinforcement learning (RL) control remains difficult. Recent work uses large language models (LLMs) to iteratively generate and refine reward functions using policy-training feedback within scripted search algorithms. However, evaluating each candidate requires a full RL...
Ming-Hao Li, Rui Tan, Rui-Hang Wang
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