What is recorded here?
Learns history-dependent recurrent policies across task distributions so fixed network weights support fast within-task adaptation through changing recurrent state.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The contemporaneous recurrent meta-reinforcement-learning approaches have separate source bylines and GRU/TRPO versus LSTM/actor-critic implementations. Their trial and episode-reset conventions matter. Near-optimal performance on the tested bandits and navigation tasks does not establish optimality on arbitrary new tasks.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Yan Duan; John Schulman; Xi Chen; Peter Bartlett; Ilya Sutskever; Pieter Abbeel; Jane X Wang; Zeb Kurth-Nelson; Dhruva Tirumala; Hubert Soyer; Joel Z Leibo; Remi Munos; Charles Blundell; Dharshan Kumaran; Matt Botvinick
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
RL-squared and recurrent deep meta-reinforcement learning
Learns history-dependent recurrent policies across task distributions so fixed network weights support fast within-task adaptation through changing recurrent state.
Scientific scope
The contemporaneous recurrent meta-reinforcement-learning approaches have separate source bylines and GRU/TRPO versus LSTM/actor-critic implementations. Their trial and episode-reset conventions matter. Near-optimal performance on the tested bandits and navigation tasks does not establish optimality on arbitrary new tasks.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Yan Duan; John Schulman; Xi Chen; Peter Bartlett; Ilya Sutskever; Pieter Abbeel; Jane X Wang; Zeb Kurth-Nelson; Dhruva Tirumala; Hubert Soyer; Joel Z Leibo; Remi Munos; Charles Blundell; Dharshan Kumaran; Matt Botvinick
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.