Adaptive Dynamic Programming Control

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• SEYED YASER NABAVI CHASHMI

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• SEYED YASER NABAVI CHASHMI

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• ÖNDER TUTSOY

Learning in a partially observable and nonstationary environment is still one of the challenging problems in the area of multiagent (MA) learning. Reinforcement learning is a generic method that suits the needs of MA learning in many aspects. This paper presents two new multiagent based domain independent coordination…

• FARUK POLAT

Learning in a partially observable and nonstationary environment is still one of the challenging problems in the area of multiagent (MA) learning. Reinforcement learning is a generic method that suits the needs of MA learning in many aspects. This paper presents two new multiagent based domain independent coordination…

• REDA ALHAJJ

Learning in a partially observable and nonstationary environment is still one of the challenging problems in the area of multiagent (MA) learning. Reinforcement learning is a generic method that suits the needs of MA learning in many aspects. This paper presents two new multiagent based domain independent coordination…

• REDA ALHAJJ

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• DUYGUN EROL BARKANA

Model-free control approaches require advanced exploration-exploitation policies to achieve practical tasks such as learning to bipedal robot walk in unstructured environments. In this article, we first construct a comprehensive exploration-exploitation policy that carries quality knowledge about the long-term predict…

• DUYGUN EROL BARKANA

Model-free control approaches require advanced exploration-exploitation policies to achieve practical tasks such as learning to bipedal robot walk in unstructured environments. In this article, we first construct a comprehensive exploration-exploitation policy that carries quality knowledge about the long-term predict…

• DUYGUN EROL BARKANA

Model-free control approaches require advanced exploration-exploitation policies to achieve practical tasks such as learning to bipedal robot walk in unstructured environments. In this article, we first construct a comprehensive exploration-exploitation policy that carries quality knowledge about the long-term predict…

• KEMAL BALIKÇI

An autonomous humanoid robot (HR) with learning and control algorithms is able to balance itself during sitting down, standing up, walking and running operations, as humans do. In this study, reinforcement learning (RL) with a complete symbolic inverse kinematic (IK) solution is developed to balance the full lower bod…

• DUYGUN EROL BARKANA
• ÖNDER TUTSOY
• ŞULE ÇOLAK

Model-free control approaches require advanced exploration-exploitation policies to achieve practical tasks such as learning to bipedal robot walk in unstructured environments. In this article, we first construct a comprehensive exploration-exploitation policy that carries quality knowledge about the long-term predict…

• ÖNDER TUTSOY