The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Deep learning, reinforcement learning, and world models
Open accessAbstract
Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the "Deep Learning and Reinforcement Learning" session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies that can be key technologies to achieve human-level intelligence.
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Human-level control through deep reinforcement learning
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Self-citationMastering the game of Go with deep neural networks and tree search
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A simple framework for contrastive learning of visual representations
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Self-citationArtificial intelligence: A modern approach
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Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717
Survey on large language model-enhanced reinforcement learning: Concept, taxonomy, and methods
Cao, Y., Zhao, H., Cheng, Y., Shu, T., Chen, Y., Liu, G., Liang, G., Zhao, J., Yan, J., & Li, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3497992 · Cited by 174
“Possessed with a powerful modeling capability and world knowledge, LLMs can serve as a world model simulator to learn complex environmental dynamics with high fidelity by iteratively predicting the next state and reward, thus increasing the sample efficiency in model-based RL [147], [149].”
Reasoning with Language Model is Planning with World Model
Hao, S., Gu, Y., Ma, H., Hong, J., Wang, Z., Wang, D., & Hu, Z. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.507 · Cited by 168
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102
“• Reinforcement Learning: Reinforcement learning, characterized by its adaptability and optimization capabilities, has become increasingly vital in decision-making and autonomous systems [176], [177].”
BackgroundOfosu-Ampong, K. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100127 · Cited by 81
Metadata
- Authors
- Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., & Morimoto, J.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 152
- Pages
- 267-275
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W4224220194
- Cited by
- 543
- References
- 82
- FWCI
- 34.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., & Morimoto, J. (2022). Deep learning, reinforcement learning, and world models. Neural Networks. https://doi.org/10.1016/j.neunet.2022.03.037
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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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