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Reinforcement Learning (RL) is a machine learning approach where an agent learns to maximize long-term rewards through interactions with an environment, utilizing concepts like states, actions, and rewards. Passive Reinforcement Learning focuses on evaluating a fixed policy without exploration, making it suitable for high-risk applications in fields like healthcare and finance. Key methods include Direct Utility Estimation and Adaptive Dynamic Programming, which help agents learn state utilities and improve

Author
Saral Udhaya
Language
EN