Language Reinforcement Learning

[TOC] Title: Guiding Pretraining in Reinforcement Learning With Large Language Models Author: Yuqing De, Jacob Andreas et. al. Publish Year: 13 Feb 2023 Review Date: Wed, Apr 5, 2023 url: https://arxiv.org/pdf/2302.06692.pdf Summary of paper Motivation intrinstically motivated exploration methods address sparse reward problem by rewarding agents for visiting novel states or transitions. Contribution we describe a method that uses background knowledge from text corpora to shape exploration. This method, call Exploring with LLMs, reward an agent for achieving goals suggested by a language model prompted with a description of agent’s current state....

[TOC] Title: Grounded Decoding Guiding Text Generation With Grounded Models for Robot Control Author: WenLong Huang et. al. Publish Year: 1 Mar, 2023 Review Date: Thu, Mar 30, 2023 url: https://arxiv.org/abs/2303.00855 Summary of paper Motivation Unfortunately, applying LLMs to settings with embodied agents, such as robots, is challenging due to their lack of experience with the physical world, inability to parse non-language observations, and ignorance of rewards or safety constraints that robots may require....