Hybrid Conference (In Person / Virtual)

International Conference on Deep Reinforcement Learning and Data Science (ICDRLDS - 27)
25th - 26th January 2027 , Pattaya, Thailand
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Call For Papers

The ICDRLDS provides a supportive platform for both experienced researchers and early-career academicians to present their work and gain recognition.

The conference covers diverse topics such as Artificial Intelligence, Data Science, Machine Learning, encouraging participation from emerging researchers and fostering academic growth.

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

01
Deep reinforcement learning applications
02
Challenges in deep reinforcement learning
03
AI for game playing and simulations
04
Real-time decision making with reinforcement learning
05
Deep reinforcement learning in robotics
06
Ethics of reinforcement learning algorithms
07
Applications of reinforcement learning in finance
08
Data efficiency in reinforcement learning models
09
Future trends in deep reinforcement learning
10
Reinforcement learning for autonomous systems
11
Multi-agent reinforcement learning frameworks
12
Reinforcement learning for healthcare applications
13
Data-driven strategies in reinforcement learning
14
Integrating deep learning with reinforcement learning
15
Reinforcement learning for optimization problems
16
Case studies in reinforcement learning applications
17
Reinforcement learning for energy management
18
AI for personalized learning experiences
19
Reinforcement learning in natural language processing
20
Collaborative reinforcement learning systems

Assessment

Submissions will be reviewed to ensure quality and relevance, with a focus on encouraging emerging research contributions. Accepted papers will be presented and considered for publication opportunities.

Registration

Early-career researchers are encouraged to register and present their work, gaining valuable feedback and academic exposure.

Publication

The conference provides opportunities for emerging researchers to publish their work in recognized platforms.