Retrieval-Augmented Decision Transformer: External Memory for In-context
  RL
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.
67077d8146c9e0a80114a1ac
Thomas Schmied
admin_assigned
2024-10-10T09:29:24.323Z
64c3849269b1a6796052eac7
/avatars/9f0c832d5b51b659c7bb83074f02a648.svg
Thomas Schmied
user
thomasschmied
67077d8146c9e0a80114a1ad
Fabian Paischer
admin_assigned
2024-10-10T09:29:18.411Z
648826f845a9218318e0272c
https://cdn-avatars.huggingface.co/v1/production/uploads/648826f845a9218318e0272c/1zUQFA7TdQ8WC9Chskx6b.jpeg
Fabian Paischer
user
paischer101
67077d8146c9e0a80114a1ae
Vihang Patil
admin_assigned
2024-10-10T09:29:12.588Z
63dfcf6742591dda0b951a5b
/avatars/3cceda20169516bd52b0d7f9090ab41e.svg
vihang patil
user
vihangp
67077d8146c9e0a80114a1af
Markus Hofmarcher
67077d8146c9e0a80114a1b0
Razvan Pascanu
admin_assigned
2024-10-10T09:29:01.158Z
64b9310403124195cd9778ec
/avatars/57c594d3d0f97d3010b15b6a0806451c.svg
Razvan Pascanu
user
razp
67077d8146c9e0a80114a1b1
Sepp Hochreiter
Retrieval-Augmented Decision Transformer: External Memory for In-context
  RL
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.
RA-DT, a retrieval-augmented decision transformer that uses external memory for in-context learning, outperforms baselines in grid-world environments and robotics simulations by leveraging relevant sub-trajectories without full episode training.
in-context learning retrieval-augmented decision transformer external memory sub-trajectories in-context RL grid-world environments robotics simulations procedural-generated video games