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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Model-Based Indirect RL: Dynamic Programming,s induced by the present action and future actions. Dynamic programming (DP) from Bellman’s principle of optimality serves as a leading method for solving such problems, which breaks down a multistage problem into a series of overlapping subproblems and solves each optimal decision recursively. In t

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Indirect RL with Function Approximation,o the dimension of state space or action space grows exponentially. To address this issue, one popular generalization technique called function approximation has been widely used in RL, in which value function and policy are approximated with proper parameterized functions. The function approximatio

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Direct RL with Policy Gradient,on any optimality condition to compute the optimal policy. One large class of direct RL algorithms belongs to first-order optimization, and how to calculate their policy gradients plays a central role in this algorithm family. Popular policy gradients include likelihood ratio gradient, natural polic

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Approximate Dynamic Programming,e-horizon control tasks are generally formulated as optimal control problems (OCPs) with the assumption that perfect deterministic models are known. Online receding horizon optimization in traditional model predictive control is a viable but computationally inefficient approach. ADP refers to a clas

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State Constraints and Safety Consideration,antees. Equipping RL/ADP with the ability to handle constrained behaviors is of practical significance in both training process and controller implementation. Basically, there are three constrained RL/ADP methods, including penalty function method, Lagrange multiplier method, and feasible descent di

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Deep Reinforcement Learning, to learn directly from measurements of raw video data without any hand-engineered features or domain heuristics. A neural network with multiple layers that replicates the structure of a human brain is an effective tool to leverage. Deep reinforcement learning (DRL), which is an in-depth combination

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Miscellaneous Topics,ng RL are mainly related to (1) how to interact with the environment more efficiently and (2) how to learn an optimal policy with a certain amount of data. Studies on the former challenge include on-policy/off-policy, stochastic exploration, sparse reward enhancement, and offline learning, while tho

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Shengbo Eben Li Begriffe. So sieht We1nert in einer puristischen Anwendung dieser Sichtweisen eine Sackgasse für künftige Forschung (. 1996, S. 10); . (1999) fragt, ob es sich bei diesen Sichtweisen nicht um „alten Wein in neuen Schläuchen“ handele, und . führt die Konjunktur des Begriffes darauf zurück, dass es z