# Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games

arXiv: https://arxiv.org/abs/2303.02160 · HTML: https://mlsanity.com/paper/2303.02160/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 98 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Multi-Game Decision Transformers](https://mlsanity.com/paper/2205.15241/) (2022)
- [Playing FPS Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05521/) (2016)
- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [Modeling Strong and Human-Like Gameplay with KL-Regularized Search](https://mlsanity.com/paper/2112.07544/) (2021)
- [Action Space Shaping in Deep Reinforcement Learning](https://mlsanity.com/paper/2004.00980/) (2020)
- [Emergence of Maps in the Memories of Blind Navigation Agents](https://mlsanity.com/paper/2301.13261/) (2023)
- [Deep Reinforcement Learning for Navigation in AAA Video Games](https://mlsanity.com/paper/2011.04764/) (2020)
- [The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/1904.10079/) (2019)
- [The MineRL BASALT Competition on Learning from Human Feedback](https://mlsanity.com/paper/2107.01969/) (2021)
- ["It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games](https://mlsanity.com/paper/2009.00541/) (2020)
- [Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation](https://mlsanity.com/paper/2105.09637/) (2021)
- [Learning Accurate and Human-Like Driving using Semantic Maps and Attention](https://mlsanity.com/paper/2007.07218/) (2020)
- [A 20-Year Community Roadmap for Artificial Intelligence Research in the US](https://mlsanity.com/paper/1908.02624/) (2019)
- [Leveraging human knowledge in tabular reinforcement learning: A study of human subjects](https://mlsanity.com/paper/1805.05769/) (2018)
- [On Multi-Agent Learning in Team Sports Games](https://mlsanity.com/paper/1906.10124/) (2019)

## Built on by (most cited first, 11 of 11)

- [Will GPT-4 Run DOOM?](https://mlsanity.com/paper/2403.05468/) (2024)
- [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)
- [Reinforcement Learning for High-Level Strategic Control in Tower Defense Games](https://mlsanity.com/paper/2406.07980/) (2024)
- [Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization](https://mlsanity.com/paper/2511.15055/) (2025)
- [Learning to Terminate in Object Navigation](https://mlsanity.com/paper/2309.16164/) (2023)
- [Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors](https://mlsanity.com/paper/2501.00078/) (2024)
- [Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach](https://mlsanity.com/paper/2506.03522/) (2025)
- [CogniPlay: a work-in-progress Human-like model for General Game Playing](https://mlsanity.com/paper/2507.05868/) (2025)
- [Eval-PPO: Building an Efficient Threat Evaluator Using Proximal Policy Optimization](https://mlsanity.com/paper/2503.12098/) (2025)
- [Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola](https://mlsanity.com/paper/2510.14154/) (2025)
- [Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization](https://mlsanity.com/paper/2605.30928/) (2026)

## Abstract (arXiv)

We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game. To this end, we propose a novel AI agent with the goal of generating more human-like behavior. We collect hundreds of crowd-sourced assessments comparing the human-likeness of navigation behavior generated by our agent and baseline AI agents with human-generated behavior. Our proposed agent passes a Turing Test, while the baseline agents do not. By passing a Turing Test, we mean that human judges could not quantitatively distinguish between videos of a person and an AI agent navigating. To understand what people believe constitutes human-like navigation, we extensively analyze the justifications of these assessments. This work provides insights into the characteristics that people consider human-like in the context of goal-directed video game navigation, which is a key step for further improving human interactions with AI agents.