# Deep Learning for Video Game Playing

arXiv: https://arxiv.org/abs/1708.07902 · HTML: https://mlsanity.com/paper/1708.07902/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2017.

## Built on by (most cited first, 30 of 30)

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- [CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning](https://mlsanity.com/paper/2203.11096/) (2022)
- [Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors](https://mlsanity.com/paper/2210.02506/) (2022)
- [High-Accuracy Model-Based Reinforcement Learning, a Survey](https://mlsanity.com/paper/2107.08241/) (2021)
- [Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter](https://mlsanity.com/paper/2208.03374/) (2022)
- [Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04067/) (2021)
- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games](https://mlsanity.com/paper/2509.03682/) (2025)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
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- [The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning](https://mlsanity.com/paper/2302.10887/) (2023)
- [Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning](https://mlsanity.com/paper/2104.08876/) (2021)
- [How to Choose a Reinforcement-Learning Algorithm](https://mlsanity.com/paper/2407.20917/) (2024)
- [Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning](https://mlsanity.com/paper/2211.13257/) (2022)
- [Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations](https://mlsanity.com/paper/2502.10303/) (2025)
- [Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-Interaction](https://mlsanity.com/paper/2104.11918/) (2021)
- [Safer Reinforcement Learning through Transferable Instinct Networks](https://mlsanity.com/paper/2107.06686/) (2021)
- [Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System](https://mlsanity.com/paper/2211.07977/) (2022)
- [User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks](https://mlsanity.com/paper/2302.01471/) (2023)
- [Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games](https://mlsanity.com/paper/2410.16653/) (2024)
- [Cultivating Game Sense for Yourself: Making VLMs Gaming Experts](https://mlsanity.com/paper/2503.21263/) (2025)
- [How LLMs are Shaping the Future of Virtual Reality](https://mlsanity.com/paper/2508.00737/) (2025)
- [Visual Radial Basis Q-Network](https://mlsanity.com/paper/2206.06712/) (2022)
- [Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem](https://mlsanity.com/paper/2310.05430/) (2023)
- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)
- [Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report](https://mlsanity.com/paper/2412.14085/) (2024)
- [Augmenting Game AI with Deep Reinforcement Learning](https://mlsanity.com/paper/2606.20210/) (2026)

## Abstract (arXiv)

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.