# Winning Isn't Everything: Enhancing Game Development with Intelligent Agents

arXiv: https://arxiv.org/abs/1903.10545 · HTML: https://mlsanity.com/paper/1903.10545/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 15 of 15)

- [Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning](https://mlsanity.com/paper/2208.07811/) (2022)
- [Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation](https://mlsanity.com/paper/2207.08379/) (2022)
- [A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents](https://mlsanity.com/paper/2204.02889/) (2022)
- [Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents](https://mlsanity.com/paper/2104.10610/) (2021)
- [Improving Generalization in Game Agents with Data Augmentation in Imitation Learning](https://mlsanity.com/paper/2309.12815/) (2023)
- [Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL](https://mlsanity.com/paper/2102.11893/) (2021)
- [Bayesian Optimization-based Search for Agent Control in Automated Game Testing](https://mlsanity.com/paper/2508.13121/) (2025)
- [Modeling Human Behavior Part I -- Learning and Belief Approaches](https://mlsanity.com/paper/2205.06485/) (2022)
- [Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data](https://mlsanity.com/paper/2401.17436/) (2024)
- [In Pursuit of Predictive Models of Human Preferences Toward AI Teammates](https://mlsanity.com/paper/2503.15516/) (2025)
- [An Appraisal Transition System for Event-driven Emotions in Agent-based Player Experience Testing](https://mlsanity.com/paper/2105.05589/) (2021)
- [TROFI: Trajectory-Ranked Offline Inverse Reinforcement Learning](https://mlsanity.com/paper/2506.22008/) (2025)
- [RuleSmith: Multi-Agent LLMs for Automated Game Balancing](https://mlsanity.com/paper/2602.06232/) (2026)
- [Augmenting Game AI with Deep Reinforcement Learning](https://mlsanity.com/paper/2606.20210/) (2026)
- [FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games](https://mlsanity.com/paper/2607.06514/) (2026)

## Abstract (arXiv)

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this paper, we study the problem of training intelligent agents in service of game development. Unlike the agents built to "beat the game", our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multi-faceted concepts with practical implications outlined in this paper. We report four case studies in which the style and skill requirements inform the choice of algorithms and metrics used to train agents; ranging from A* search to state-of-the-art deep reinforcement learning. We, further, show that the learning potential of state-of-the-art deep RL models does not seamlessly transfer from the benchmark environments to target ones without heavily tuning their hyperparameters, leading to linear scaling of the engineering efforts and computational cost with the number of target domains.