title: Diffusion Augmented Agents: A Framework for Efficient Exploration and
    Transfer Learning
summary: We introduce Diffusion Augmented Agents (DAAG), a novel framework that
  leverages large language models, vision language models, and diffusion models
  to improve sample efficiency and transfer learning in reinforcement learning
  for embodied agents. DAAG hindsight relabels the agent's past experience by
  using diffusion models to transform videos in a temporally and geometrically
  consistent way to align with target instructions with a technique we call
  Hindsight Experience Augmentation. A large language model orchestrates this
  autonomous process without requiring human supervision, making it well-suited
  for lifelong learning scenarios. The framework reduces the amount of
  reward-labeled data needed to 1) finetune a vision language model that acts as
  a reward detector, and 2) train RL agents on new tasks. We demonstrate the
  sample efficiency gains of DAAG in simulated robotics environments involving
  manipulation and navigation. Our results show that DAAG improves learning of
  reward detectors, transferring past experience, and acquiring new tasks - key
  abilities for developing efficient lifelong learning agents. Supplementary
  material and visualizations are available on our website
  https://sites.google.com/view/diffusion-augmented-agents/
paper_authors._id: 66a9c389f47cae66bc0e3178
paper_authors.name: Norman Di Palo
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-07-31T04:54:37.097Z
paper_authors._id: 62dac377f014388f908974f4
paper_authors.avatarUrl: /avatars/39bf0ca206441575a45f577060cdd8bc.svg
paper_authors.fullname: Norman Di Palo
paper_authors.type: user
paper_authors.user: normandipalo
paper_authors._id: 66a9c389f47cae66bc0e3179
paper_authors.name: Leonard Hasenclever
paper_authors._id: 66a9c389f47cae66bc0e317a
paper_authors.name: Jan Humplik
paper_authors._id: 66a9c389f47cae66bc0e317b
paper_authors.name: Arunkumar Byravan
paper_title: Diffusion Augmented Agents: A Framework for Efficient Exploration and
    Transfer Learning
paper_summary: We introduce Diffusion Augmented Agents (DAAG), a novel framework that
  leverages large language models, vision language models, and diffusion models
  to improve sample efficiency and transfer learning in reinforcement learning
  for embodied agents. DAAG hindsight relabels the agent's past experience by
  using diffusion models to transform videos in a temporally and geometrically
  consistent way to align with target instructions with a technique we call
  Hindsight Experience Augmentation. A large language model orchestrates this
  autonomous process without requiring human supervision, making it well-suited
  for lifelong learning scenarios. The framework reduces the amount of
  reward-labeled data needed to 1) finetune a vision language model that acts as
  a reward detector, and 2) train RL agents on new tasks. We demonstrate the
  sample efficiency gains of DAAG in simulated robotics environments involving
  manipulation and navigation. Our results show that DAAG improves learning of
  reward detectors, transferring past experience, and acquiring new tasks - key
  abilities for developing efficient lifelong learning agents. Supplementary
  material and visualizations are available on our website
  https://sites.google.com/view/diffusion-augmented-agents/
paper_ai_summary: Diffusion Augmented Agents (DAAG) enhances reinforcement learning for embodied agents using large and vision language models, diffusion models, and hindsight experience augmentation to improve sample efficiency and transfer learning.
paper_ai_keywords: diffusion models reinforcement learning embodied agents large language models vision language models hindsight relabeling hindsight experience augmentation sample efficiency transfer learning lifelong learning reward detector simulated robotics environments manipulation navigation