The DOI matches its Crossref record; 157 of the 157 references it cites were found in a registry or an index.
Deep Learning for Video Game Playing
Open accessAbstract
In this paper, 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.
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Cites
Deep Residual Learning for Image Recognition
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
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Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
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(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
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Mastering the game of Go with deep neural networks and tree search
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(2016) · Cited by 11,679
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Transfer Learning in Deep Reinforcement Learning: A Survey
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3292075 · Cited by 861
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
Synthetic Data for Deep Learning
Nikolenko, S. I. (2021) · Springer Optimization and Its Applications · doi:10.1007/978-3-030-75178-4 · Cited by 423
PCGRL: Procedural Content Generation via Reinforcement Learning
Khalifa, A., Bontrager, P., Earle, S., & Togelius, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment · doi:10.1609/aiide.v16i1.7416 · Cited by 156
Self-citationA bird's-eye view of deep learning in bioimage analysis
Meijering, E. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.08.003 · Cited by 126
Metadata
- Authors
- Justesen, N., Bontrager, P., Togelius, J., & Risi, S.
- Venue
- IEEE Transactions on Games
- Published
- 2020-03
- Volume
- 12
- Issue
- 1
- Pages
- 1-20
- ISSN
- 2475-1502 (ISSN-L)
- 2475-1510
- OpenAlex
- W2753316839
- Cited by
- 257
- References
- 157
- FWCI
- 16.00
- Type
- journal article
- Language
- en
- Open access
- green
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- Justesen, N., Bontrager, P., Togelius, J., & Risi, S. (2020). Deep Learning for Video Game Playing. IEEE Transactions on Games. https://doi.org/10.1109/tg.2019.2896986
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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