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While reinforcement learning (RL) holds great potential for decision making in the real world, it suffers from a number of unique difficulties which often need specific consideration.
Evolutionsstrategie : Optimierung technischer systeme nach prinzipien der biologischen evolution
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R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
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Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Bradly C. Stadie, Sergey Levine, and Pieter Abbeel · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nati Srebro, and Benjamin Recht · 2017
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Performance comparision of different momentum techniques on deep reinforcement learning
Mehmet Sarigül and Mutlu Avci · 2017
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Learning to optimize
Ke Li and Jitendra Malik · 2017
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Ilya Loshchilov and Frank Hutter · 2017
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Cyclical learning rates for training neural networks
Leslie N. Smith · 2017
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#Exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
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Disentangling the causes of plasticity loss in neural networks, 2024
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