Hybrid Conferencee

International Conference on Reinforcement Learning and Data Science (ICRLDS - 26)

23rd - 24th October 2026 | La Condamine, Monaco

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Conference Notifications:

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Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in La Condamine. 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:
"Abstract submissions for the La Condamine event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the La Condamine conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in La Condamine, 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 La Condamine."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICRLDS 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 Artificial Intelligence,Data Science,Machine Learning, 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 data optimization
02
Applications of data science in reinforcement learning
03
Multi-agent systems in reinforcement learning
04
Exploration vs exploitation in learning algorithms
05
Deep reinforcement learning for complex tasks
06
Real-world applications of reinforcement learning
07
Data-driven decision making in uncertain environments
08
Reinforcement learning for robotics and automation
09
Policy gradient methods in data science
10
Transfer learning in reinforcement learning
11
Reinforcement learning for game AI development
12
Ethical considerations in reinforcement learning
13
Combining reinforcement learning with supervised learning
14
Reinforcement learning in financial modeling
15
Adaptive learning systems using reinforcement techniques
16
Challenges in scaling reinforcement learning algorithms
17
Reinforcement learning for personalized recommendations
18
Data efficiency in reinforcement learning methods
19
Reinforcement learning for healthcare applications
20
Future trends in reinforcement learning research