LaGO: Latent Action Guidance for Online Reinforcement Learning

The paper introduces Latent Action Guidance for Online Reinforcement Learning (LaGO), which utilizes a pretrained large language model (LLM) to provide latent action priors that enhance online policy optimization, rather than functioning as a direct controller. Experiments on the CLEVR-Robot and Meta-World benchmarks reveal that LaGO improves average success rates significantly, from 15.1% to 27.2% on CLEVR-Robot and from 2.7% to 15.2% on Meta-World, indicating that leveraging LLMs can effectively enhance planning and decision-making in reinforcement learning contexts. This approach may offer practitioners a more reliable method for integrating LLMs into reinforcement learning frameworks.