Article

R²E: A multi-role LLM evolutionary framework for automated reward function design

The authors propose R²E, a framework for automated reward function design that uses explicit role specialization to mitigate insufficient exploration and optimization instability in LLM-based reward design. The framework includes a novelty explorer responsible for expanding the search space, a guardian responsible for conservative refinements to improve training stability, and an evolver that recombines high-performing rewards through crossover and mutation, with a dynamic scheduling strategy that gradually shifts the search from broad exploration to focused exploitation. R²E also introduces a global elite pool that retains the best-performing rewards to guide subsequent iterations. Experiments on multiple robotic tasks show the effectiveness of the method.

Part of the event: R²E: A Multi-Role LLM Evolutionary Framework for Automatic Reward Function Design

This page provides a summary and source link only.