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Deep Reinforcement Learning: From Q-Learning to Deep Q-Learning
2017As the two hottest branches of machine learning, deep learning and reinforcement learning both play a vital role in the field of artificial intelligence. Combining deep learning with reinforcement learning, deep reinforcement learning is a method of artificial intelligence that is much closer to human learning.
Fuxiao Tan, Pengfei Yan, Xinping Guan
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Machine learning for microbiologists
Nature Reviews Microbiology, 2023Francesco Asnicar +2 more
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Machine learning methods to model multicellular complexity and tissue specificity
Nature Reviews Materials, 2021Rachel S G Sealfon +2 more
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Machine learning sheds light on microbial dark proteins
Nature Reviews Microbiology, 2023A T Hammack +2 more
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Deep learning shapes single-cell data analysis
Nature Reviews Molecular Cell Biology, 2022Qin Ma, Dong Xu
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Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
IEEE Communications Surveys and Tutorials, 2021Latif U Khan, Walid Saad, Zhu Han
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Complexity and Cooperation in Q-Learning
1991Publisher Summary This chapter describes two cooperative learning algorithms that can reduce search and decouple the learning rate from state-space size. The first algorithm, called Learning with an External Critic (LEC), is based on the idea of a mentor who watches the learner and generates immediate rewards in response to its most recent actions ...
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2020
The purpose of the article is to analyze existing approaches of different states and actions spaces representations for Q-learning algorithm for protein structure folding problem, reveal their advantages and disadvantages and propose the new geometric ???state-space??? representation.
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The purpose of the article is to analyze existing approaches of different states and actions spaces representations for Q-learning algorithm for protein structure folding problem, reveal their advantages and disadvantages and propose the new geometric ???state-space??? representation.
openaire +1 more source