What is recorded here?
A model need not paint every future image to estimate whether a situation is promising. The Predictron learns an internal sequence of abstract states, rewards and discounts, combines predictions at several depths and trains them against return targets. Its internal states need to support useful value predictions rather than reproduce the visible environment exactly. Silver and colleagues test the approach on maze-related predictions and simulated pool. The contribution is this learned value-prediction architecture and its training design.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The main formulation predicts values in uncontrolled reward processes, rather than supplying a general action-conditional planner. A limited pool experiment ranks candidate initial settings; pocketing-event counts are not automatically percentages of successful episodes. Internal consistency supplements observed supervision and does not prove the model represents true physical dynamics. Learned depth weights do not themselves show that unused computation was skipped. Existing temporal-difference ideas, recurrent networks and later planning systems retain separate credit.
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People and systems
David Silver; Hado van Hasselt; Matteo Hessel; Tom Schaul; Arthur Guez; Tim Harley; Gabriel Dulac-Arnold; David Reichert; Neil Rabinowitz; Andre Barreto; Thomas Degris
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.
Research locations and affiliations
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Documented countries / regions: United Kingdom
Original DeepMind byline explicitly London. Work affiliation, not nationality or ownership.
English catalogue narrative
Predictron: predicting value through an abstract internal rollout
A model need not paint every future image to estimate whether a situation is promising. The Predictron learns an internal sequence of abstract states, rewards and discounts, combines predictions at several depths and trains them against return targets. Its internal states need to support useful value predictions rather than reproduce the visible environment exactly. Silver and colleagues test the approach on maze-related predictions and simulated pool. The contribution is this learned value-prediction architecture and its training design.
Scientific scope
The main formulation predicts values in uncontrolled reward processes, rather than supplying a general action-conditional planner. A limited pool experiment ranks candidate initial settings; pocketing-event counts are not automatically percentages of successful episodes. Internal consistency supplements observed supervision and does not prove the model represents true physical dynamics. Learned depth weights do not themselves show that unused computation was skipped. Existing temporal-difference ideas, recurrent networks and later planning systems retain separate credit.
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
David Silver; Hado van Hasselt; Matteo Hessel; Tom Schaul; Arthur Guez; Tim Harley; Gabriel Dulac-Arnold; David Reichert; Neil Rabinowitz; Andre Barreto; Thomas Degris
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.