Reinforcement Learning Algorithms
This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Special Issue Information
Dear Colleagues,
Reinforcement learning is a modern machine learning paradigm which implies gaining experience on run-time and using it to learn the relationship between input and output sets. It is particularly useful for modern robotic applications as well as softbot systems to let robotic and software agents smartly interact with their living/operating environments; this enables them to take the environmental events as stimuli to decide the best reaction to the environment, in response to what is ongoing within the environment. The main principle behind reinforcement learning is to let agents learn how to act optimally in order to maximise the environmental reward generated in response to its actions. Reinforcement learning is a research hot topic in AI, machine learning, and data science studies. A Special Issue on the advancements in reinforcement learning research will help update the current level of the state-of-the-art approaches, technologies, and applications in this regard. We are seeking recent research results in reinforcement learning.
Dr. Mehmet Aydin
Guest Editor
Manuscript Submission Information
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Keywords
- Reinforcement learning
- Q-leanring
- Temporal differences
- Markovien decision processes
- Deep reinforcement learning
- Deep Q-network algorithm
- Policy optimisation
- Policy based reinforcement learning
- Actor-critic RL algorithms
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Published Papers
Algorithms - ISSN 1999-4893