Description: A subgoal in the context of reinforcement learning refers to an intermediate goal that an agent must achieve to facilitate the attainment of a larger or final goal. These subgoals are crucial because they allow complex tasks to be broken down into more manageable parts, making it easier for the agent to learn and make decisions. By establishing subgoals, the agent can be guided toward the main objective more efficiently, as each subgoal provides a reward signal that helps reinforce the desired behavior. Additionally, subgoals can help improve exploration of the solution space, as the agent can focus on achieving smaller, specific goals before tackling the larger challenge. This strategy not only optimizes the learning process but can also increase the robustness of the agent in changing or uncertain environments. In summary, subgoals are fundamental tools in reinforcement learning, as they structure the learning process and enable agents to develop complex skills more effectively.
