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

arXiv: https://arxiv.org/abs/2105.09637 · HTML: https://mlsanity.com/paper/2105.09637/

## 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 (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow](https://mlsanity.com/paper/1810.00821/) (2018)
- [Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation](https://mlsanity.com/paper/1911.07450/) (2019)
- [Deep Reinforcement Learning for Navigation in AAA Video Games](https://mlsanity.com/paper/2011.04764/) (2020)
- [Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling](https://mlsanity.com/paper/1910.04289/) (2019)
- [Adversarial CAPTCHAs](https://mlsanity.com/paper/1901.01107/) (2019)
- [InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations](https://mlsanity.com/paper/1703.08840/) (2017)
- [Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic](https://mlsanity.com/paper/1805.03735/) (2018)

## Built on by (most cited first, 17 of 17)

- [Direct Behavior Specification via Constrained Reinforcement Learning](https://mlsanity.com/paper/2112.12228/) (2021)
- [Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games](https://mlsanity.com/paper/2307.11105/) (2023)
- [Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games](https://mlsanity.com/paper/2303.02160/) (2023)
- [A Rubric for Human-like Agents and NeuroAI](https://mlsanity.com/paper/2212.04401/) (2022)
- [BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks](https://mlsanity.com/paper/2312.02405/) (2023)
- [CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories](https://mlsanity.com/paper/2202.10057/) (2022)
- [Graph augmented Deep Reinforcement Learning in the GameRLand3D environment](https://mlsanity.com/paper/2112.11731/) (2021)
- [Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games](https://mlsanity.com/paper/2309.15484/) (2023)
- [Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization](https://mlsanity.com/paper/2511.15055/) (2025)
- [Memory, Space, and Planning: Multiscale Predictive Representations](https://mlsanity.com/paper/2401.09491/) (2024)
- [Aligning Agents like Large Language Models](https://mlsanity.com/paper/2406.04208/) (2024)
- [Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach](https://mlsanity.com/paper/2506.03522/) (2025)
- [Adapting Vision-Language Models for Evaluating World Models](https://mlsanity.com/paper/2506.17967/) (2025)
- [Human-Like Navigation Behavior: A Statistical Evaluation Framework](https://mlsanity.com/paper/2203.05965/) (2022)
- [Effective Reward Specification in Deep Reinforcement Learning](https://mlsanity.com/paper/2412.07177/) (2024)
- [Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization](https://mlsanity.com/paper/2605.30928/) (2026)
- [Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26](https://mlsanity.com/paper/2607.07498/) (2026)

## 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.