Reinforcement Learning for Autonomous Systems: Applications and Challenges
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Abstract
Reinforcement Learning (RL) has emerged as a powerful tool for training autonomous systems, from self-driving cars to robotics and intelligent agents. This paper examines the principles of RL, including policy optimization and reward-based learning, and their applications in real-world scenarios. Key challenges such as scalability, safety, and the exploration-exploitation trade-off are analyzed. The study also explores advancements in multi-agent RL and its implications for collaborative decision-making. Future directions for integrating RL with other AI paradigms to enhance system robustness and adaptability are discussed.
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