What is recorded here?
A game-playing agent learns more than a final score when it also practices useful auxiliary tasks. UNREAL adds pixel-change control, reward prediction and replayed value learning to an A3C agent, sharing parts of the visual and recurrent representation. These extra learning signals help the agent use its experience more effectively in the reported Labyrinth and Atari experiments. The contribution is the combined auxiliary-learning architecture and its finite evaluation, building on existing actor–critic, replay and off-policy methods.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The base task still uses external rewards; “unsupervised” auxiliary learning is not a reward-free general agent. Reported Labyrinth mean performance rises from 54% to 87% on a human-normalized scale, about 1.61 times rather than more than twice. Step-efficiency estimates depend on a specified A3C target, selected runs and aggregation across tasks; they are not general wall-clock savings. Game scores do not measure general intelligence. Some alternative auxiliary losses hurt final performance, and feature-control results are preliminary.
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People and systems
Max Jaderberg; Volodymyr Mnih; Wojciech Marian Czarnecki; Tom Schaul; Joel Z Leibo; David Silver; Koray Kavukcuoglu
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, UK. Work affiliation, not nationality or ownership.
English catalogue narrative
UNREAL: auxiliary learning tasks that improve a control representation
A game-playing agent learns more than a final score when it also practices useful auxiliary tasks. UNREAL adds pixel-change control, reward prediction and replayed value learning to an A3C agent, sharing parts of the visual and recurrent representation. These extra learning signals help the agent use its experience more effectively in the reported Labyrinth and Atari experiments. The contribution is the combined auxiliary-learning architecture and its finite evaluation, building on existing actor–critic, replay and off-policy methods.
Scientific scope
The base task still uses external rewards; “unsupervised” auxiliary learning is not a reward-free general agent. Reported Labyrinth mean performance rises from 54% to 87% on a human-normalized scale, about 1.61 times rather than more than twice. Step-efficiency estimates depend on a specified A3C target, selected runs and aggregation across tasks; they are not general wall-clock savings. Game scores do not measure general intelligence. Some alternative auxiliary losses hurt final performance, and feature-control results are preliminary.
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
Max Jaderberg; Volodymyr Mnih; Wojciech Marian Czarnecki; Tom Schaul; Joel Z Leibo; David Silver; Koray Kavukcuoglu
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.