International Conference on Reinforcement Learning and Data Science ICRLDS

Call For Papers

The ICRLDS bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Artificial Intelligence, Data Science, Machine Learning , encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

Reinforcement learning algorithms for data optimization

Applications of data science in reinforcement learning

Multi-agent systems in reinforcement learning

Exploration vs exploitation in learning algorithms

Deep reinforcement learning for complex tasks

Real-world applications of reinforcement learning

Data-driven decision making in uncertain environments

Reinforcement learning for robotics and automation

Policy gradient methods in data science

Transfer learning in reinforcement learning

Reinforcement learning for game AI development

Ethical considerations in reinforcement learning

Combining reinforcement learning with supervised learning

Reinforcement learning in financial modeling

Adaptive learning systems using reinforcement techniques

Challenges in scaling reinforcement learning algorithms

Reinforcement learning for personalized recommendations

Data efficiency in reinforcement learning methods

Reinforcement learning for healthcare applications

Future trends in reinforcement learning research

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in reputed journals and conference proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

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Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

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Hybrid Event