Bachelor Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 1.0, University of Duisburg-Essen, language: English, abstract: Reinforcement learning is a learning problem in which an actor has to behave optimally in its environment. Deep learning methods, on the other hand, are a subclass of representation learning, which in turn focuses on extracting the necessary features for the task (e.g. classification or detection). As such, they serve as powerful function approximators. The combination of those two paradigm results in deep reinforcement learning. This thesis gives an overview of the recent advancement in the field. The results are divided into two broad research directions: value-based and policy-based approaches. This research shows several algorithms from those directions and how they perform. Finally, multiple open research questions are addressed and new research directions are proposed.
A Review of Recent Advancements in Deep Reinforcement Learning
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Bachelor Thesis from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 1.0, University of Duisburg-Essen, language: English, abstract: Reinforcement learning is a learning problem in which an actor has to behave optimally in its environment. Deep learning methods, on the other hand, are a subclass of representation learning, which in turn focuses on extracting the necessary features for the task (e.g. classification or detection). As such, they serve as powerful function approximators. The combination of those two paradigm results in deep reinforcement learning. This thesis gives an overview of the recent advancement in the field. The results are divided into two broad research directions: value-based and policy-based approaches. This research shows several algorithms from those directions and how they perform. Finally, multiple open research questions are addressed and new research directions are proposed.
Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network ...
This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of...
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks...
Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has...
The applications of rapidly advancing intelligent systems are so varied that many are still yet to be discovered. There is often a disconnect between experts in computer science, artificial...
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