Titlebook: Reinforcement Learning for Sequential Decision and Optimal Control; Shengbo Eben Li Textbook 2023 The Editor(s) (if applicable) and The Au

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978-981-19-7786-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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Creditee

Model-Free Indirect RL: Monte Carlo,its environment exploration does not need to traverse the whole state space; and it is often less negatively impacted by the violation of the Markov property. However, MC estimation suffers from very slow convergence due to the demand for sufficient exploration and restricted application on episodic and small-scale tasks.

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Miscellaneous Topics, how to learn with fewer samples, how to learn rewards from experts, how to solve multi-agent games, and how to learn from offline data. The state-of-the-art RL frameworks, libraries, and simulation platforms are also briefly described to support the R&D of more advanced RL algorithms.

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Principles of RL Problems,o, it generally contains four key elements: state-action samples, a policy, reward signals, and an environment model. In most stochastic tasks, the value function is defined as the expectation of the long-term return, which is used to evaluate how good a policy is. It naturally holds a recursive rel

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Model-Free Indirect RL: Temporal Difference, to update the current value function. Therefore, TD learning methods can learn from incomplete episodes or continuing tasks in a step-by-step manner since it can update the value function based on its current estimate. As stated by Andrew Barto and Richard Sutton, if one had to identify one idea as