New HRLLI Framework Enhances RL Efficiency with Adaptive Language Guidance

Researchers have introduced Hierarchical Reinforcement Learning with Language Instructions (HRLLI), a novel framework designed to improve the sample efficiency of reinforcement learning agents. HRLLI decomposes natural language instructions into stage-specific guidance elements, allowing a high-level policy to select the most relevant instruction piece for the current environment state. This selected guidance then informs a low-level policy that executes actions, enabling adaptive grounding of language into decisions. Experiments on the RTFM benchmark demonstrated that HRLLI significantly outperforms existing instruction-conditioned RL baselines. AI

IMPACT This framework could lead to more sample-efficient AI agents capable of understanding and acting upon complex, stage-dependent instructions.

RANK_REASON The cluster contains an academic paper detailing a new framework for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Hierarchical Reinforcement Learning with Language Instructions
• reinforcement learning
• RTFM benchmark
• Select-to-Act

• paper
• model release

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