Reward Function
In the context of reinforcement learning, a reward function is a mathematical function that maps the state-action pairs of an agent to a scalar value, which represents the desirability of that state-action pair. The goal of the agent is to learn a policy that maximizes the expected cumulative reward over time. The reward function is a crucial component of the reinforcement learning framework, as it defines the task that the agent is trying to solve. The reward function can be designed to encourage the agent to achieve a specific goal, avoid certain behaviors, or balance multiple objectives.
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