Description: A delayed reward is a reward that is received after a series of actions, rather than immediately. This concept is fundamental in reinforcement learning, an area of artificial intelligence that focuses on how agents can learn to make decisions through interaction with an environment. In this context, delayed rewards allow agents to evaluate the value of their actions based on long-term consequences, rather than focusing solely on immediate rewards. This feature is crucial for developing more complex and effective strategies, as it encourages planning and anticipation of future outcomes. Delayed rewards also reflect the nature of many real-life situations, where decisions can have effects that do not manifest immediately. Therefore, understanding and applying this concept is essential for designing learning algorithms capable of solving complex problems and adapting to dynamic environments.
