# Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach

arXiv: https://arxiv.org/abs/2506.03522 · HTML: https://mlsanity.com/paper/2506.03522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Open-Ended Learning Leads to Generally Capable Agents](https://mlsanity.com/paper/2107.12808/) (2021)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Synthetic Experience Replay](https://mlsanity.com/paper/2303.06614/) (2023)
- [A Non-Parametric Test to Detect Data-Copying in Generative Models](https://mlsanity.com/paper/2004.05675/) (2020)
- [Counter-Strike Deathmatch with Large-Scale Behavioural Cloning](https://mlsanity.com/paper/2104.04258/) (2021)
- [Deep Reinforcement Learning for Navigation in AAA Video Games](https://mlsanity.com/paper/2011.04764/) (2020)
- [T-Rep: Representation Learning for Time Series using Time-Embeddings](https://mlsanity.com/paper/2310.04486/) (2023)
- ["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)
- [Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games](https://mlsanity.com/paper/2303.02160/) (2023)
- [Learning to Reach Goals via Diffusion](https://mlsanity.com/paper/2310.02505/) (2023)
- [PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems](https://mlsanity.com/paper/1910.12454/) (2019)
- [Model-Based and Model-Free point prediction algorithms for locally stationary random fields](https://mlsanity.com/paper/2212.03079/) (2022)
- [Predictive inference for locally stationary time series with an application to climate data](https://mlsanity.com/paper/1712.02383/) (2017)
- [The NPC AI of The Last of Us: A case study](https://mlsanity.com/paper/2207.00682/) (2022)

## Built on by (most cited first, 1 of 1)

- [Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola](https://mlsanity.com/paper/2510.14154/) (2025)

## Abstract (arXiv)

Navigation path traces play a crucial role in video game design, serving as a vital resource for both enhancing player engagement and fine-tuning non-playable character behavior. Generating such paths with human-like realism can enrich the overall gaming experience, and evaluating path traces can provide game designers insights into player interactions. Despite the impressive recent advancements in deep learning-based generative modeling, the video game industry hesitates to adopt such models for path generation, often citing their complex training requirements and interpretability challenges. To address these problems, we propose a novel path generation and evaluation approach that is grounded in principled nonparametric statistics and provides precise control while offering interpretable insights. Our path generation method fuses two statistical techniques: (1) nonparametric model-free transformations that capture statistical characteristics of path traces through time; and (2) copula models that capture statistical dependencies in space. For path evaluation, we adapt a nonparametric three-sample hypothesis test designed to determine if the generated paths are overfit (mimicking the original data too closely) or underfit (diverging too far from it). We demonstrate the precision and reliability of our proposed methods with empirical analysis on two existing gaming benchmarks to showcase controlled generation of diverse navigation paths. Notably, our novel path generator can be fine-tuned with user controllable parameters to create navigation paths that exhibit varying levels of human-likeness in contrast to those produced by neural network-based agents. The code is available at https://github.com/daniel-campa/mf-copula.