mlsanityData to 7 Oct 2026

"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games

arXiv 2009.00541 ↗First posted 1 Sep 2020cs.AIcited by 17

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Abstract (arXiv)

Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games industry evolves to adopt machine learning (ML) more widely, it is vital that the research community learn from the best practices cultivated within the industry over decades creating agents. However, although commercial game agent creation pipelines are more mature than those based on ML, opportunities for improvement still abound. As a foundation for shared progress identifying research opportunities between researchers and practitioners, we interviewed seventeen game agent creators from AAA studios, indie studios, and industrial research labs about the challenges they experienced with their professional workflows. Our study revealed several open challenges ranging from design to implementation and evaluation. We compare with literature from the research community that address the challenges identified and conclude by highlighting promising directions for future research supporting agent creation in the games industry.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2020.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 2; 2024: 1; 2025: 6; 2026: 1.

17 papers.

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  1. 2021Direct Behavior Specification via Constrained Reinforcement Learningcited by 30
  2. 2022Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learningcited by 13
  3. 2023Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Gamescited by 10
  4. 2023Visual Encoders for Data-Efficient Imitation Learning in Modern Video Gamescited by 6
  5. 2021Policy Fusion for Adaptive and Customizable Reinforcement Learning Agentscited by 4
  6. 2022Deep Reinforcement Learning for Multi-Agent Interactioncited by 3
  7. 2021Procedural Content Generation using Behavior Trees (PCGBT)cited by 2
  8. 2021Player Modeling using Behavioral Signals in Competitive Online Gamescited by 2
  9. 2025Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approachcited by 2
  10. 2021Empirically Evaluating Creative Arc Negotiation for Improvisational Decision-makingcited by 1
  11. 2025Improving Sample Efficiency in Multi-Agent Reinforcement Learning for Simulated Football Games via Explorationcited by 1
  12. 2025Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approachcited by 1
5 more
  1. 2025Self-correcting Reward Shaping via Language Models for Reinforcement Learning Agents in Gamescited by 1
  2. 2024A Research Agenda for Usability and Generalisation in Reinforcement Learningcited by 0
  3. 2025TROFI: Trajectory-Ranked Offline Inverse Reinforcement Learningcited by 0
  4. 2025Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Scholacited by 0
  5. 2026Augmenting Game AI with Deep Reinforcement Learningcited by 0