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Microsoft Research Summit 2021 • Videos

Research talk: Evaluating human-like navigation in 3D video games

On the path to developing agents that learn complex human-like behavior, a key challenge is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited. The researchers address these limitations through a novel automated Navigation Turing Test (NTT) that learns to predict human judgments of human-likeness. They demonstrate the effectiveness of their automated NTT on a navigation task in a complex 3D environment. They investigated six classification models to shed light on the types of architectures best suited to this task, and they validated them against data collected through a human NTT. The best models achieve high accuracy when distinguishing true human and agent behavior. At the same time, the researchers show that predicting finer-grained human assessment of agents’ progress towards human-like behavior remains unsolved. Their work takes an important step towards agents that more effectively learn complex human-like behavior.

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Event:
Microsoft Research Summit 2021
Track:
Reinforcement Learning
Date:
Speakers:
Raluca Georgescu, Ida Momennejad
Affiliation:
Microsoft Research Cambridge, Microsoft Research NYC

Reinforcement Learning