Hybrid Conferencee

International Conference on Reinforcement Learning for Control Systems (ICRLCS - 26)

6th - 7th November 2026 | Paris, France

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Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Paris. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
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Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Paris conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Paris, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Paris."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICRLCS aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

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

01
Reinforcement learning algorithms for control systems
02
Applications of RL in robotics and automation
03
Model-free vs model-based reinforcement learning
04
Safety and reliability in RL control systems
05
Multi-agent reinforcement learning techniques
06
Deep reinforcement learning for complex tasks
07
Real-time decision making using RL
08
Transfer learning in reinforcement learning
09
Reward shaping strategies in RL applications
10
Adaptive control using reinforcement learning
11
Challenges in implementing RL in practice
12
Reinforcement learning for dynamic systems
13
Combining RL with traditional control methods
14
Benchmarking RL algorithms in control scenarios
15
Applications of RL in aerospace systems
16
Reinforcement learning for energy management
17
Human-in-the-loop reinforcement learning
18
Scalability issues in RL for control systems
19
Reinforcement learning in uncertain environments
20
Future directions in RL for control systems