Synthesis of model predictive control and reinforcement learning: Survey and classification
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Abstract
Model predictive control (MPC) and reinforcement learning (RL) are two successful control techniques for Markov decision processes. Both approaches are derived from similar fundamental principles, and both are widely used in practical applications, including robotics, process control, energy systems, and autonomous driving. Despite their similarities, MPC and RL follow distinct paradigms that emerged from diverse communities and different requirements. Various technical discrepancies, particularly the role of an environment model as part of the algorithm, lead to methodologies with nearly complementary advantages. Due to their orthogonal benefits, research interest in combination methods has recently increased…
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Authors
9Topics & keywords
Topics
Keywords
- Reinforcement learning
- Model predictive control
- Leverage (statistics)
- Markov decision process
- Categorization
- Process (computing)
- Set (abstract data type)
UN Sustainable Development Goals
- Peace, Justice and strong institutions
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