New RM-VLM approach uses LLMs and VLMs to automate reward machine generation

Researchers have introduced RM-VLM, a novel approach that leverages large language models (LLMs) and vision-language models (VLMs) to automatically generate reward machines (RMs) from natural-language task descriptions. This method addresses the limitation of manually engineering proposition-grounding oracles for RMs in reinforcement learning. RM-VLM uses an LLM to create the RM and a VLM to ground its propositions in image-based observations, enabling RM-guided policy learning. The system has demonstrated improved task success rates in domains including grid-world navigation, robotic manipulation, and 3D object collection compared to existing VLM-based reward methods. AI

IMPACT Automates reward machine generation for reinforcement learning tasks, potentially improving agent performance and reducing manual engineering effort.

RANK_REASON The cluster contains a research paper detailing a novel approach to reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• 3D object collection
• arXiv
• grid-world navigation
• large language model
• Markov Chains
• reinforcement learning
• Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
• RM-VLM
• Robotic Manipulation
• vision-language model

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →