Jing_cheng_pang Natural Language Conditioned Reinforcement Learning With Inside Out Task Language Development and Translation 2023

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1. Title: Jing_cheng_pang Natural Language Conditioned Reinforcement Learning With Inside Out Task Language Development and Translation 2023
2. Author: Jing-Cheng Pang et. al.
3. Publish Year: 18 Feb 2023
4. Review Date: Fri, Mar 3, 2023
5. url: https://arxiv.org/pdf/2302.09368.pdf

Summary of paper

Motivation

• previous approaches generally implemented language-conditioned RL by providing human instructions in natural language and training a following policy
  • this is outside-in approach
  • the policy needs to comprehend the NL and manage the task simultaneously.
  • However, the unbounded NL examples often bring much extra complexity for solving concrete RL tasks, which can distract policy learning from completing the task

Contribution

• we investigate an inside-out scheme for natural language-conditioned RL by developing a task language (TL) that is task-related and unique. The TL is used in RL to achieve high effective policy training.
• besides, a translator is trained to translate NL into TL.
• experiments indicate that the new model not only better comprehends NL instructions but also leads to better instruction following policy that improves 13.4% success rate and adapts to unseen expressions of NL instruction.
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