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High-Accuracy Model-Based Reinforcement Learning, a Survey
[article]
2021
arXiv
pre-print
Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems from game playing and robotics have been solved with deep model-free methods. Unfortunately, the sample complexity of model-free methods is often high. To reduce the number of environment samples, model-based reinforcement learning creates an explicit model of the environment dynamics. Achieving high model accuracy is a challenge in high-dimensional problems. In
arXiv:2107.08241v1
fatcat:tma6xb2uy5fybjfhmzasfx2cta