# Augmenting Game AI with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2606.20210 · HTML: https://mlsanity.com/paper/2606.20210/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 30 of 32)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [A Study on Overfitting in Deep Reinforcement Learning](https://mlsanity.com/paper/1804.06893/) (2018)
- [Bigger, Better, Faster: Human-level Atari with human-level efficiency](https://mlsanity.com/paper/2305.19452/) (2023)
- [Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control](https://mlsanity.com/paper/2405.16158/) (2024)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code](https://mlsanity.com/paper/2405.15568/) (2024)
- [Counter-Strike Deathmatch with Large-Scale Behavioural Cloning](https://mlsanity.com/paper/2104.04258/) (2021)
- [Deep Learning for Video Game Playing](https://mlsanity.com/paper/1708.07902/) (2017)
- [Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning](https://mlsanity.com/paper/2209.08483/) (2022)
- [Deep Reinforcement Learning for Navigation in AAA Video Games](https://mlsanity.com/paper/2011.04764/) (2020)
- [NitroGen: An Open Foundation Model for Generalist Gaming Agents](https://mlsanity.com/paper/2601.02427/) (2026)
- [Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem](https://mlsanity.com/paper/2402.02868/) (2024)
- ["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)
- [Winning Isn't Everything: Enhancing Game Development with Intelligent Agents](https://mlsanity.com/paper/1903.10545/) (2019)
- [Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning](https://mlsanity.com/paper/2208.07811/) (2022)
- [Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games](https://mlsanity.com/paper/2307.11105/) (2023)
- [Scaling Behavior Cloning Improves Causal Reasoning: An Open Model for Real-Time Video Game Playing](https://mlsanity.com/paper/2601.04575/) (2026)
- [Replay across Experiments: A Natural Extension of Off-Policy RL](https://mlsanity.com/paper/2311.15951/) (2023)
- [SPEQ: Offline Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning](https://mlsanity.com/paper/2501.08669/) (2025)
- [DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games](https://mlsanity.com/paper/2012.01914/) (2020)
- [Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents](https://mlsanity.com/paper/2104.10610/) (2021)
- [Optimistic critics can empower small actors](https://mlsanity.com/paper/2506.01016/) (2025)
- [Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning](https://mlsanity.com/paper/2112.13744/) (2021)
- [Generating Personas for Games with Multimodal Adversarial Imitation Learning](https://mlsanity.com/paper/2308.07598/) (2023)
- [FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functions](https://mlsanity.com/paper/2504.10002/) (2025)
- [Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play](https://mlsanity.com/paper/2311.17190/) (2023)
- [Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach](https://mlsanity.com/paper/2510.23216/) (2025)

## 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.