title: Retrieval-Augmented Decision Transformer: External Memory for In-context
    RL
summary: In-context learning (ICL) is the ability of a model to learn a new task by
  observing a few exemplars in its context. While prevalent in NLP, this
  capability has recently also been observed in Reinforcement Learning (RL)
  settings. Prior in-context RL methods, however, require entire episodes in the
  agent's context. Given that complex environments typically lead to long
  episodes with sparse rewards, these methods are constrained to simple
  environments with short episodes. To address these challenges, we introduce
  Retrieval-Augmented Decision Transformer (RA-DT). RA-DT employs an external
  memory mechanism to store past experiences from which it retrieves only
  sub-trajectories relevant for the current situation. The retrieval component in
  RA-DT does not require training and can be entirely domain-agnostic. We
  evaluate the capabilities of RA-DT on grid-world environments, robotics
  simulations, and procedurally-generated video games. On grid-worlds, RA-DT
  outperforms baselines, while using only a fraction of their context length.
  Furthermore, we illuminate the limitations of current in-context RL methods on
  complex environments and discuss future directions. To facilitate future
  research, we release datasets for four of the considered environments.
paper_authors._id: 67077d8146c9e0a80114a1ac
paper_authors.name: Thomas Schmied
paper_authors.status: admin_assigned
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paper_authors.fullname: Thomas Schmied
paper_authors.type: user
paper_authors.user: thomasschmied
paper_authors._id: 67077d8146c9e0a80114a1ad
paper_authors.name: Fabian Paischer
paper_authors.status: admin_assigned
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paper_authors.fullname: Fabian Paischer
paper_authors.type: user
paper_authors.user: paischer101
paper_authors._id: 67077d8146c9e0a80114a1ae
paper_authors.name: Vihang Patil
paper_authors.status: admin_assigned
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paper_authors._id: 63dfcf6742591dda0b951a5b
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paper_authors.fullname: vihang patil
paper_authors.type: user
paper_authors.user: vihangp
paper_authors._id: 67077d8146c9e0a80114a1af
paper_authors.name: Markus Hofmarcher
paper_authors._id: 67077d8146c9e0a80114a1b0
paper_authors.name: Razvan Pascanu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-10T09:29:01.158Z
paper_authors._id: 64b9310403124195cd9778ec
paper_authors.avatarUrl: /avatars/57c594d3d0f97d3010b15b6a0806451c.svg
paper_authors.fullname: Razvan Pascanu
paper_authors.type: user
paper_authors.user: razp
paper_authors._id: 67077d8146c9e0a80114a1b1
paper_authors.name: Sepp Hochreiter
paper_title: Retrieval-Augmented Decision Transformer: External Memory for In-context
    RL
paper_summary: In-context learning (ICL) is the ability of a model to learn a new task by
  observing a few exemplars in its context. While prevalent in NLP, this
  capability has recently also been observed in Reinforcement Learning (RL)
  settings. Prior in-context RL methods, however, require entire episodes in the
  agent's context. Given that complex environments typically lead to long
  episodes with sparse rewards, these methods are constrained to simple
  environments with short episodes. To address these challenges, we introduce
  Retrieval-Augmented Decision Transformer (RA-DT). RA-DT employs an external
  memory mechanism to store past experiences from which it retrieves only
  sub-trajectories relevant for the current situation. The retrieval component in
  RA-DT does not require training and can be entirely domain-agnostic. We
  evaluate the capabilities of RA-DT on grid-world environments, robotics
  simulations, and procedurally-generated video games. On grid-worlds, RA-DT
  outperforms baselines, while using only a fraction of their context length.
  Furthermore, we illuminate the limitations of current in-context RL methods on
  complex environments and discuss future directions. To facilitate future
  research, we release datasets for four of the considered environments.
paper_ai_keywords: in-context learning (ICL) Reinforcement Learning (RL) episodes sparse rewards Retrieval-Augmented Decision Transformer (RA-DT) external memory mechanism sub-trajectories domain-agnostic grid-world environments robotics simulations procedurally-generated video games