Reinforcement Learning in Robotics: From Simulation to Real-World Applications
Abstract
Reinforcement Learning (RL) has gained significant attention in the field of robotics as a promising approach for enabling robots to acquire complex skills and adapt to dynamic environments. This paper provides an in-depth exploration of the transition of RL techniques from simulation environments to real-world applications, highlighting the challenges and opportunities inherent in this journey. We discuss the theoretical foundations of RL, the importance of simulated training scenarios, and the key considerations in transferring learned policies to physical robots. This paper presents a comprehensive review of recent advancements, case studies, and practical strategies for successfully implementing RL in robotics.
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