Abstract
The spectacular success enjoyed by machine learning (ML), primarily driven by deep neural networks can arguably be interpreted as only the tip of the iceberg. As neural network architectures, algorithmic methods, and computational power evolve, the scope of the problems that ML addresses will continue to grow. One such direction for growth materializes in reinforcement learning.
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Cansever, D. (2021). What May Lie Ahead in Reinforcement Learning. In: Vamvoudakis, K.G., Wan, Y., Lewis, F.L., Cansever, D. (eds) Handbook of Reinforcement Learning and Control. Studies in Systems, Decision and Control, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-60990-0_1
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DOI: https://doi.org/10.1007/978-3-030-60990-0_1
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