In machine learning, a policy refers to a set of rules or strategies that an agent follows to make decisions in an environment in order to maximize its performance on a specific task. In reinforcement learning, a policy is often represented as a mapping from states to actions. The goal of the agent is to learn an optimal policy that maximizes a predefined reward function over time. The optimal policy can be learned using various algorithms, such as Q-learning, policy gradient methods, or Monte Carlo methods. The performance of a policy can be evaluated using metrics such as cumulative reward, success rate, or mean-squared error.
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