Registration summary
ConferenceICRLML
ModeStandard / Physical
ParticipationListener
Registration fee$135.00
Bank charges (5.8%)$7.83
Total payable$142.83
Includes all bank processing charges — the amount above is exactly what will be charged.
Benefits of Registering as Listener
Access to Conference Sessions
Networking Opportunities
Certificate of Participation
Invitation Letter Support
Conference Kit / Materials
Access to Keynote Sessions
• Conference Session Tracks •
SDG-Aligned Research Themes
ICRLML conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.
01 Advancements in Deep Reinforcement Learning +
This track focuses on the latest developments in deep reinforcement learning techniques, emphasizing novel architectures and algorithms. Researchers are encouraged to present their findings on how deep learning can enhance the performance of reinforcement learning systems.
02 Policy Optimization Techniques +
This session will explore various policy optimization methods, including actor-critic approaches and policy gradient algorithms. Contributions that demonstrate improvements in convergence rates and stability in training are particularly welcome.
03 Value Function Approximation Methods +
This track addresses innovative approaches to value function approximation, including linear and non-linear methods. Papers that discuss the trade-offs between accuracy and computational efficiency in real-world applications are encouraged.
04 Exploration-Exploitation Strategies +
This session will delve into strategies for balancing exploration and exploitation in reinforcement learning frameworks. Contributions that propose new algorithms or theoretical insights into this fundamental challenge are highly sought after.
05 Adaptive Agents in Dynamic Environments +
This track focuses on the development of adaptive agents capable of learning and evolving in dynamic environments. Researchers are invited to share their work on methodologies that enable agents to adjust their strategies in response to changing conditions.
06 Multi-Agent Reinforcement Learning +
This session will explore the complexities and challenges of multi-agent reinforcement learning systems. Papers that investigate cooperation, competition, and communication among agents are particularly encouraged.
07 Reward Shaping and Its Applications +
This track examines the role of reward shaping in enhancing learning efficiency and agent performance. Contributions that provide theoretical insights or practical applications of reward shaping techniques are welcome.
08 Model-Free vs. Model-Based Learning +
This session will compare and contrast model-free and model-based reinforcement learning approaches. Researchers are invited to present empirical studies or theoretical analyses that highlight the strengths and weaknesses of each paradigm.
09 Reinforcement Learning in Robotics +
This track focuses on the application of reinforcement learning techniques in robotic systems. Papers that demonstrate how RL can improve robotic decision-making and control in real-world scenarios are encouraged.
10 Reinforcement Learning for Game AI +
This session will explore the application of reinforcement learning in developing intelligent game agents. Contributions that showcase innovative techniques for enhancing gameplay through RL are particularly welcome.
11 Decision Making and Control Systems +
This track addresses the integration of reinforcement learning techniques in decision-making and control systems. Researchers are invited to present their work on RL applications that improve system performance and reliability.
