Description: Expected reward is a fundamental concept in reinforcement learning, referring to the anticipation of the reward that can be obtained by performing a specific action within a given environment, based on the agent’s current policy. This value is calculated by considering both the immediate reward that can be received after the action and the future rewards that can be obtained from subsequent decisions. The expected reward allows reinforcement learning agents to evaluate and compare different actions, guiding their behavior towards those that will maximize their long-term performance. This approach is based on the idea that agents must learn to make optimal decisions through experience, adjusting their policy based on the rewards received. The expected reward is commonly represented by the value function, which estimates the total value that can be obtained from a particular state, considering all possible actions and their consequences. This concept is crucial for the development of reinforcement learning algorithms, as it provides a mathematical foundation for decision-making in complex and dynamic environments, where the consequences of actions are not always immediate or evident.
