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Call For Papers

The ICRLCS 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 Data Science, 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 control systems
  • Applications of RL in robotics and automation
  • Model-free vs model-based reinforcement learning
  • Safety and reliability in RL control systems
  • Multi-agent reinforcement learning techniques
  • Deep reinforcement learning for complex tasks
  • Real-time decision making using RL
  • Transfer learning in reinforcement learning
  • Reward shaping strategies in RL applications
  • Adaptive control using reinforcement learning
  • Challenges in implementing RL in practice
  • Reinforcement learning for dynamic systems
  • Combining RL with traditional control methods
  • Benchmarking RL algorithms in control scenarios
  • Applications of RL in aerospace systems
  • Reinforcement learning for energy management
  • Human-in-the-loop reinforcement learning
  • Scalability issues in RL for control systems
  • Reinforcement learning in uncertain environments
  • Future directions in RL for control systems

Evaluation

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

Registration

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

Publication

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

Submit Your Paper Now Confirm Your Participation Today
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Conference Alert

Due to heightened regional tensions and travel risks, the conference may be conducted in virtual-only mode. Updates regarding participation format will be communicated in advance.