Model-Based Reinforcement Learning: From Data to Continuous Actions with a Python-based Toolbox 1st Edition

Capture the future of intelligent control with Model-Based Reinforcement Learning: From Data to Continuous Actions with a Python-based Toolbox, 1st Edition by Jun Liu and Milad Farsi. This authoritative guide turns advanced theory into practical capability, making model-based RL accessible for researchers, engineers, and graduate students.

Step into a clear, example-driven journey that bridges data and continuous-action decision-making. The authors demystify core concepts—system identification, dynamics modeling, planning under uncertainty, and policy optimization—while showing how these pieces fit together in real-world applications such as robotics, autonomous vehicles, finance, and energy systems. Explanations are precise, with intuitive illustrations that help you move from mathematical foundations to applied solutions.

Imagine building controllers that learn from sensor data and safely produce smooth control actions: this book shows how. It highlights modern algorithms and trade-offs, emphasizes sample efficiency, and focuses on stability and interpretability—key concerns for deployment in industry and research labs across North America, Europe, and Asia. The integrated Python-based toolbox is presented step-by-step so you can follow workflows and reproduce experiments within your own projects and datasets.

Whether you are a practitioner aiming to implement scalable controllers or a student seeking a rigorous introduction to continuous-action reinforcement learning, this volume offers depth without unnecessary complexity. Authored by experienced researchers Jun Liu and Milad Farsi, the text balances theoretical rigor with practical insight.

Advance your career and your projects: add Model-Based Reinforcement Learning: From Data to Continuous Actions with a Python-based Toolbox to your library and start turning data into dependable, continuous-control solutions today.

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