mlsanityData to 14 Oct 2026

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

arXiv 2105.09637 ↗First posted 20 May 2021cs.AI cs.LGcited by 17

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Abstract (arXiv)

A key challenge on the path to developing agents that learn complex human-like behavior 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. We address these limitations through a novel automated Navigation Turing Test (ANTT) that learns to predict human judgments of human-likeness. We demonstrate the effectiveness of our automated NTT on a navigation task in a complex 3D environment. We investigate six classification models to shed light on the types of architectures best suited to this task, and validate them against data collected through a human NTT. Our best models achieve high accuracy when distinguishing true human and agent behavior. At the same time, we show that predicting finer-grained human assessment of agents' progress towards human-like behavior remains unsolved. Our work takes an important step towards agents that more effectively learn complex human-like behavior.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 20 May 2021.

Builds on

14 of 36 references.

References · 14

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  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  7. 2020Object Goal Navigation using Goal-Oriented Semantic Explorationcited by 429
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  9. 2019Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigationcited by 40
  10. 2020Deep Reinforcement Learning for Navigation in AAA Video Gamescited by 27
  11. 2019Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labelingcited by 22
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2 more
  1. 2017InfoGAIL: Interpretable Imitation Learning from Visual Demonstrationscited by 11
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 4; 2024: 3; 2025: 3; 2026: 2.

17 papers.

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  1. 2021Direct Behavior Specification via Constrained Reinforcement Learningcited by 31
  2. 2023Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Gamescited by 12
  3. 2023Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Gamescited by 11
  4. 2022A Rubric for Human-like Agents and NeuroAIcited by 8
  5. 2023BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Taskscited by 8
  6. 2022CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectoriescited by 6
  7. 2021Graph augmented Deep Reinforcement Learning in the GameRLand3D environmentcited by 5
  8. 2023Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Gamescited by 4
  9. 2025Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantizationcited by 3
  10. 2024Memory, Space, and Planning: Multiscale Predictive Representationscited by 2
  11. 2024Aligning Agents like Large Language Modelscited by 1
  12. 2025Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approachcited by 1
5 more
  1. 2025Adapting Vision-Language Models for Evaluating World Modelscited by 1
  2. 2022Human-Like Navigation Behavior: A Statistical Evaluation Frameworkcited by 0
  3. 2024Effective Reward Specification in Deep Reinforcement Learningcited by 0
  4. 2026Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantizationcited by 0
  5. 2026Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26cited by 0