Teaching Embodied Reinforcement Learning Agents: Informativeness and
  Diversity of Language Use
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
6724f6630d9a15815c043988
Jiajun Xi
6724f6630d9a15815c043989
Yinong He
6724f6630d9a15815c04398a
Jianing Yang
claimed_verified
2024-11-03T14:20:57.022Z
646afdd535c7a57f936d3ff5
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/LsDQPftQrnp_Gvanzim4J.jpeg
Jed Yang
user
jedyang97
6724f6630d9a15815c04398b
Yinpei Dai
6724f6630d9a15815c04398c
Joyce Chai
Teaching Embodied Reinforcement Learning Agents: Informativeness and
  Diversity of Language Use
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
Using diverse and informative language feedback enhances generalization and fast adaptation in reinforcement learning for embodied agents.
reinforcement learning (RL) embodied agents language inputs language informativeness language diversity generalization fast adaptation