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For The Win: population-trained visual team play

Combined concurrent populations, two-tier optimization of internal reward schemes and multiscale recurrent representations to learn pixel-based QuakeIII capture-the-flag policies that cooperated and competed with unfamiliar teammates/opponents.

What is recorded here?

Combined concurrent populations, two-tier optimization of internal reward schemes and multiscale recurrent representations to learn pixel-based QuakeIII capture-the-flag policies that cooperated and competed with unfamiliar teammates/opponents.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Original2018v1 study, not a silent merge with expanded2019journal scenarios. Uses game-point event signals and evolved reward shaping, not pixels plus terminal win alone. Human/agent control resolution, reaction time and tagging accuracy differ; artificial accuracy reduction tests do not prove identical interfaces or universal human-level teamwork. PBT and earlier recurrent/RL ideas are inherited.

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People and systems

Max Jaderberg; Wojciech M. Czarnecki; Iain Dunning; Luke Marris; Guy Lever; Antonio Garcia Castaneda; Charles Beattie; Neil C. Rabinowitz; Ari S. Morcos; Avraham Ruderman; Nicolas Sonnerat; Tim Green; Louise Deason; Joel Z. Leibo; David Silver; Demis Hassabis; Koray Kavukcuoglu; Thore Graepel

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.

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English catalogue narrative

For The Win: population-trained visual team play

Combined concurrent populations, two-tier optimization of internal reward schemes and multiscale recurrent representations to learn pixel-based QuakeIII capture-the-flag policies that cooperated and competed with unfamiliar teammates/opponents.

Scientific scope

Original2018v1 study, not a silent merge with expanded2019journal scenarios. Uses game-point event signals and evolved reward shaping, not pixels plus terminal win alone. Human/agent control resolution, reaction time and tagging accuracy differ; artificial accuracy reduction tests do not prove identical interfaces or universal human-level teamwork. PBT and earlier recurrent/RL ideas are inherited.

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; Wojciech M. Czarnecki; Iain Dunning; Luke Marris; Guy Lever; Antonio Garcia Castaneda; Charles Beattie; Neil C. Rabinowitz; Ari S. Morcos; Avraham Ruderman; Nicolas Sonnerat; Tim Green; Louise Deason; Joel Z. Leibo; David Silver; Demis Hassabis; Koray Kavukcuoglu; Thore Graepel

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.

Original sources