Exploring Reinforcement Learning Algorithms and Applications: A Review
Abstract
Reinforcement learning (RL) has gained prominence as a powerful paradigm for training agents to interact with dynamic environments and learn optimal decision-making policies. This review paper offers an overview of RL algorithms, including value-based, policy-based, and actor-critic methods. It examines the applications of RL in robotics, autonomous systems, and game playing, along with challenges and future directions in RL research.
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