arXiv Machine Learning
Jun 25
Low-Complexity Policy Tessellations in Structured Markov Decision Processes
arXiv:2606. 25593v1 Announce Type: new Abstract: We study optimal-policy geometry in structured Markov decision processes.
Low-Complexity Policy Tessellations in Structured Markov Decision Processes
We study optimal-policy geometry in structured Markov decision processes. While approximate dynamic programming and reinforcement learning typically approximate high-dimensional value functions, we show that optimal policies induce simpler decision tessellations.
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Jun 23
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).