What is recorded here?
Combined deterministic policy gradients with a distributional critic, distributed experience collection, multi-step targets and prioritized replay for continuous-control learning.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
A synthesis extending DDPG and distributional RL, not invention of their components. Reported robustness and benchmark gains are empirical, not general continuous-control optimality or safety.
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
Gabriel Barth-Maron; Matthew W. Hoffman; David Budden; Will Dabney; Dan Horgan; Dhruva TB; Alistair Muldal; Nicolas Heess; Timothy Lillicrap
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
D4PG: distributed deterministic actors with a distributional critic
Combined deterministic policy gradients with a distributional critic, distributed experience collection, multi-step targets and prioritized replay for continuous-control learning.
Scientific scope
A synthesis extending DDPG and distributional RL, not invention of their components. Reported robustness and benchmark gains are empirical, not general continuous-control optimality or safety.
People and systems
Gabriel Barth-Maron; Matthew W. Hoffman; David Budden; Will Dabney; Dan Horgan; Dhruva TB; Alistair Muldal; Nicolas Heess; Timothy Lillicrap