Guided Soft Actor Critic: A Guided Deep Reinforcement Learning Approach for Partially Observable Markov Decision Processes
Introduces Guided Soft Actor-Critic (GSAC), in which a guidance module steers Soft Actor-Critic during training to cope with partial observability, improving sample efficiency and final return on partially observable continuous-control tasks.
Research area: Reinforcement Learning & Autonomous Systems.
Dr. Mehmet Haklıdır is the founding Director of the TÜBİTAK BİLGEM Artificial Intelligence Institute, Türkiye’s first national AI institute. He holds a Ph.D. in Control and Automation Engineering from Istanbul Technical University and has more than 24 years of experience in AI, autonomous systems, big data, simulation, and technology policy.
He contributed to Türkiye’s first National AI Strategy and led the technical preparation and coordination of the National Artificial Intelligence Action Plan 2026–2030, finalized through consultation with the national AI ecosystem. He serves on the Steering Committee of the Science, Technology and Innovation Policies Council of the Presidency of the Republic of Türkiye and is a member of several international AI governance initiatives, including OECD ONE AI, GPAI, UNESCO AI Ethics, NATO DAIRB, and the European AI Alliance.
His current research focuses on large language models, AI safety with an emphasis on agentic AI systems, deep reinforcement learning, robotics, and autonomous decision-making under uncertainty.