title: Teaching Embodied Reinforcement Learning Agents: Informativeness and
    Diversity of Language Use
summary: In real-world scenarios, it is desirable for embodied agents to have the
  ability to leverage human language to gain explicit or implicit knowledge for
  learning tasks. Despite recent progress, most previous approaches adopt simple
  low-level instructions as language inputs, which may not reflect natural human
  communication. It's not clear how to incorporate rich language use to
  facilitate task learning. To address this question, this paper studies
  different types of language inputs in facilitating reinforcement learning (RL)
  embodied agents. More specifically, we examine how different levels of language
  informativeness (i.e., feedback on past behaviors and future guidance) and
  diversity (i.e., variation of language expressions) impact agent learning and
  inference. Our empirical results based on four RL benchmarks demonstrate that
  agents trained with diverse and informative language feedback can achieve
  enhanced generalization and fast adaptation to new tasks. These findings
  highlight the pivotal role of language use in teaching embodied agents new
  tasks in an open world. Project website:
  https://github.com/sled-group/Teachable_RL
paper_authors._id: 6724f6630d9a15815c043988
paper_authors.name: Jiajun Xi
paper_authors._id: 6724f6630d9a15815c043989
paper_authors.name: Yinong He
paper_authors._id: 6724f6630d9a15815c04398a
paper_authors.name: Jianing Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-03T14:20:57.022Z
paper_authors._id: 646afdd535c7a57f936d3ff5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/LsDQPftQrnp_Gvanzim4J.jpeg
paper_authors.fullname: Jed Yang
paper_authors.type: user
paper_authors.user: jedyang97
paper_authors._id: 6724f6630d9a15815c04398b
paper_authors.name: Yinpei Dai
paper_authors._id: 6724f6630d9a15815c04398c
paper_authors.name: Joyce Chai
paper_title: Teaching Embodied Reinforcement Learning Agents: Informativeness and
    Diversity of Language Use
paper_summary: In real-world scenarios, it is desirable for embodied agents to have the
  ability to leverage human language to gain explicit or implicit knowledge for
  learning tasks. Despite recent progress, most previous approaches adopt simple
  low-level instructions as language inputs, which may not reflect natural human
  communication. It's not clear how to incorporate rich language use to
  facilitate task learning. To address this question, this paper studies
  different types of language inputs in facilitating reinforcement learning (RL)
  embodied agents. More specifically, we examine how different levels of language
  informativeness (i.e., feedback on past behaviors and future guidance) and
  diversity (i.e., variation of language expressions) impact agent learning and
  inference. Our empirical results based on four RL benchmarks demonstrate that
  agents trained with diverse and informative language feedback can achieve
  enhanced generalization and fast adaptation to new tasks. These findings
  highlight the pivotal role of language use in teaching embodied agents new
  tasks in an open world. Project website:
  https://github.com/sled-group/Teachable_RL
paper_ai_keywords: reinforcement learning (RL) embodied agents language inputs language informativeness past behaviors future guidance language diversity language expressions agent learning agent inference empirical results generalization fast adaptation