Solving Markov Decision Processes with Future Information via MPC
Model Predictive Control (MPC) is widely used in industrial and robotic systems for enforcing constraints and embedding domain knowledge through finite-horizon optimization-based planning. However, despite these strengths, an MPC scheme typically does not yield optimal policies for sequential decision-making problems formulated as Markov Decision Processes (MDPs). Recent combinations of MPC with Reinforcement Learning (RL) alleviate this issue by treating MPC as a parameterized model of the optimal policy of an MDP and adjusting its parameters using data. While these approaches typically consider classical MDPs, many real-world problems include future information--such as forecasts, price...
| Authors | Shambhuraj Sawant, Akhil S Anand, Dirk Reinhardt et al. |
|---|---|
| Primary category | eess.SY |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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