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International Conference on Reinforcement Learning and Machine Learning - (ICRLML-27)

13th - 14th January 2027 , Sreemangal Upazila - Bangladesh

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Call For Papers

The (ICRLML-27) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Machine Learning are explored to promote cross-domain knowledge exchange and collaborative innovation.

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

  • Applications of reinforcement learning in robotics
  • Deep reinforcement learning techniques
  • Multi-agent reinforcement learning systems
  • Reinforcement learning for game AI
  • Challenges in reinforcement learning applications
  • Reinforcement learning in autonomous vehicles
  • Policy gradient methods in RL
  • Reinforcement learning for resource management
  • Exploration vs exploitation in RL
  • Transfer learning in reinforcement learning
  • Reinforcement learning for recommendation systems
  • Real-world applications of RL algorithms
  • Reinforcement learning in healthcare optimization
  • Ethics of reinforcement learning applications
  • Reinforcement learning for financial trading
  • Hierarchical reinforcement learning approaches
  • Reinforcement learning in natural language processing
  • Robustness of RL algorithms in practice
  • Reinforcement learning for energy management
  • Future trends in reinforcement learning research

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.

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