Studying topics separately followed by learning about them together has been a recurring theme in this book. We first looked at model-based algorithms in Chapter 3. In this setup, we knew the model dynamics of the world the agent was operating in. The agent used the knowledge of model dynamics along with Bellman equations to first carry out the evaluation/prediction task to learn the state or state-action values. It then followed this up by improving the policy to get the optimal behavior, which ...
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2024
N. SanghiDeep Reinforcement Learning with Pythonhttps://doi.org/10.1007/979-8-8688-0273-7_1010. Integrated Planning and Learning
Nimish Sanghi1
(1)
Bangalore, India
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