Reinforcement Learning and Dynamic Programming Using Function Approximators by Lucian Busoniu
From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. This title provides a comprehensive exploration of the field of Dynamic Programming (DP) and Reinforcement Learning (RL).-
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Advances in Missile Guidance, Control, and Estimation
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Robust Formation Control for Multiple Unmanned Aerial Vehicles
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Analysis and Synthesis of Fuzzy Control Systems
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Control of Nonlinear Systems via PI, PD and PID
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Wireless Ad hoc and Sensor Networks
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Modeling and Control of Complex Systems
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Distributed Adaptive Consensus Control of Uncertain Multi-Agent Systems
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Optimal Event-Triggered Control Using Adaptive Dynamic Programming
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Control of Nonlinear Systems
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Maneuverable Formation Control in Constrained Space
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Quantitative Process Control Theory
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Modeling and Control for Micro/Nano Devices and Systems
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Sliding Mode Control in Electro-Mechanical Systems
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System Modeling and Control with Resource-Oriented Petri Nets
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Variable Gain Control and Its Applications in Energy Conversion
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Optimal Control
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Cooperative Control of Multi-Agent Systems
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Multi-Agent Systems
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Deterministic Learning Theory for Identification, Recognition, and Control
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Synchronization and Control of Multiagent Systems
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Anti-Disturbance Control for Systems with Multiple Disturbances
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Tensor Product Model Transformation in Polytopic Model-Based Control
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Fundamentals in Modeling and Control of Mobile Manipulators
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Intelligent Diagnosis and Prognosis of Industrial Networked Systems
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Nonlinear Control of Dynamic Networks
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End-to-End Adaptive Congestion Control in TCP/IP Networks
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Electric and Plug-in Hybrid Vehicle Networks
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Fuzzy Controller Design
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Modern Control Engineering
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Nonlinear Control of Electric Machinery
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Discrete-Time Recurrent Neural Control
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Nonlinear Pinning Control of Complex Dynamical Networks
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Classical Feedback Control with Nonlinear Multi-Loop Systems
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Adaptive Control of Dynamic Systems with Uncertainty and Quantization
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Optimal Networked Control Systems with MATLAB
Robert Babuska, Lucian Busoniu, and Bart de Schutter are with the Delft University of Technology. Damien Ernst is with the University of Liege.
| SKU | Unavailable |
| ISBN 13 | 9781439821084 |
| ISBN 10 | 1439821089 |
| Title | Reinforcement Learning and Dynamic Programming Using Function Approximators |
| Author | Lucian Busoniu |
| Series | Automation And Control Engineering |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | CRC Press |
| Year published | 2010-04-29 |
| Number of pages | 280 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |