Abdelhadi Larach

About

Abdelhadi Larach is a researcher whose work lies at the intersection of stochastic optimization, robotics, and artificial intelligence. His primary research focus is on developing advanced algorithms for Markov Decision Processes (MDPs), particularly in solving large-scale, complex decision-making problems under uncertainty. Larach’s major contributions include pioneering accelerated decomposition techniques for large discounted MDPs, which leverage hierarchical state-space partitions into strongly connected components to dramatically improve computational efficiency—a foundational approach cited 10 times. He has also innovatively applied MDPs to real-world robotics, proposing a novel coverage path planning model for autonomous demining robots, enabling optimal exploration of unknown hazardous environments. Further extending the theoretical boundaries, Larach introduced a transformed Stochastic Shortest Path framework that addresses dead ends and energy constraints, maximizing target-reaching probability while minimizing expected costs. Though his citation counts are modest, his work demonstrates significant technical depth and practical relevance, bridging theoretical MDP advancements with critical applications in autonomous systems and resource-constrained planning.

Research Focus

Key Achievements

Top Papers

1. 1
   Accelerated decomposition techniques for large discounted Markov decision processes

   10 citations · 2017
2. 2
   A Markov Decision Model for Area Coverage in Autonomous Demining Robot

   2 citations · 2017
3. 3
   A New Transformed Stochastic Shortest Path with Dead Ends and Energy Constraint

   2 citations · 2019

Key Collaborators

Contact & Links