Topic: Advances in Reinforcement Learning for Robotic Systems

The field of robotics is undergoing a transformative phase, driven by rapid advancements in artificial intelligence, particularly in reinforcement learning. Reinforcement learning has emerged as a pivotal approach for endowing robots with the ability to learn complex behaviors through interactions with their environment. This paradigm shift enables robots to perform tasks with increased autonomy and adaptability across diverse and dynamic settings.

This Special Issue focuses on the latest advancements in reinforcement learning applied to robotic systems. We aim to compile state-of-the-art research that highlights both theoretical innovations and practical applications, offering a comprehensive view of the current trends and future directions in this vibrant field.

This Special Issue invites high-quality, original research articles, review papers, and case studies that focus on, but are not limited to, the following topics:
● Deep reinforcement learning and its applications in robotics;
● Multi-agent reinforcement learning for collaborative robotic systems;
● Hierarchical reinforcement learning methods for complex task decomposition;
● Sim-to-real transfer techniques for reinforcement learning in robotics;
● Applications of reinforcement learning in industrial automation;
● Reinforcement learning for humanoid and legged robots;
● Safe and ethical reinforcement learning approaches in robotic decision-making;
● Integration of reinforcement learning with other artificial intelligence paradigms in robotics;
● Real-time learning and adaptation in dynamic environments;
● Benchmarking and evaluation methodologies for reinforcement learning-based robotic systems.

Deep learning, reinforcement learning, robotic systems, multi-agent, autonomy and adaptability

30 Jun 2025