#control-tasks2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago3

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RF-Agent: Automated Reward Function Design via Language Agent Tree Search

Researchers introduce RF-Agent, a framework that uses Large Language Models as agents to automatically design reward functions for control tasks through Monte Carlo Tree Search. The method improves upon existing approaches by better utilizing historical feedback and enhancing search efficiency across 17 diverse low-level control tasks.

AIBullisharXiv – CS AI · 4h ago6

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Automating the Refinement of Reinforcement Learning Specifications

Researchers introduce AutoSpec, a framework that automatically refines reinforcement learning specifications to help AI agents learn complex tasks more effectively. The system improves coarse-grained logical specifications through exploration-guided strategies while maintaining specification soundness, demonstrating promising improvements in solving complex control tasks.