mlsanityData to 14 Oct 2026

Augmenting Game AI with Deep Reinforcement Learning

arXiv 2606.20210 ↗First posted 18 Jun 2026cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

Immersion in video games depends not only on graphics, audio, and game mechanics, but also on the quality of in-game characters. Producing believable characters, or game AI, remains a significant challenge as behavioral complexity is hard to capture with hand-coded systems. Game AI is a source of immersion and engagement; however, the limitations stemming from the challenges of creating game AI often lead to frustration and the breaking of the illusion of realism within the game. The introduction of machine learning models opens the door to creating more believable, authentic, and relatable characters in games. The promise is that they either learn from interacting with the game, or from player data, to develop true human-like behavior. In this paper, we envision more applications of reinforcement learning for game AI in the future. For this to materialize, current research limitations are prohibitive to broad deployment across game genres. Therefore, we propose a framework for training reinforcement learning models with a set of requirements in mind that are suited towards game AI and game development. We present examples of games with reinforcement learning-augmented game AI and describe the practicalities of deploying player-facing machine learning agents in modern games. Furthermore, we identify bottlenecks and hard problems in these areas, which we believe offer promising research directions to accelerate the adoption of machine learning in game AI for the video game industry.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 32 of its 38 references that are in the map. First posted on arXiv 18 Jun 2026.

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32 of 38 references.

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  1. 2025Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach27 Oct

Other references · 31

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  3. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  4. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
  5. 2018A Study on Overfitting in Deep Reinforcement Learningcited by 174
  6. 2023Bigger, Better, Faster: Human-level Atari with human-level efficiencycited by 147
  7. 2024Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous controlcited by 108
  8. 2024Large Language Models and Games: A Survey and Roadmapcited by 93
  9. 2024OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Codecited by 74
  10. 2021Counter-Strike Deathmatch with Large-Scale Behavioural Cloningcited by 50
  11. 2017Deep Learning for Video Game Playingcited by 30
  12. 2022Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learningcited by 28
19 more
  1. 2020Deep Reinforcement Learning for Navigation in AAA Video Gamescited by 27
  2. 2026NitroGen: An Open Foundation Model for Generalist Gaming Agentscited by 27
  3. 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problemcited by 23
  4. 2020"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Gamescited by 17
  5. 2019Winning Isn't Everything: Enhancing Game Development with Intelligent Agentscited by 15
  6. 2022Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learningcited by 13
  7. 2023Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Gamescited by 12
  8. 2026Scaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playingcited by 11
  9. 2023Replay across Experiments: A Natural Extension of Off-Policy RLcited by 10
  10. 2025SPEQ: Offline Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learningcited by 7
  11. 2020DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Gamescited by 6
  12. 2021Policy Fusion for Adaptive and Customizable Reinforcement Learning Agentscited by 4
  13. 2025Optimistic critics can empower small actorscited by 4
  14. 2021Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learningcited by 3
  15. 2023Generating Personas for Games with Multimodal Adversarial Imitation Learningcited by 3
  16. 2025FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functionscited by 3
  17. 2023Minimax Exploiter: A Data Efficient Approach for Competitive Self-Playcited by 2
  18. 2024Efficient Visibility Approximation for Game AI using Neural Omnidirectional Distance Fieldscited by 1
  19. 2024Human-like Bots for Tactical Shooters Using Compute-Efficient Sensorscited by 1

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