In Reinforcement Learning, what is the difference between exploration and exploitation?

In Reinforcement Learning, the distinction between exploration and exploitation is crucial for developing effective learning strategies.

Exploration refers to the process of trying out new actions in order to gather more information about the environment and potential rewards. This is important because, without exploration, an agent may miss out on discovering better strategies or options that could lead to higher rewards. By experimenting with different actions, the agent expands its knowledge and understanding of the environment.

Exploitation, on the other hand, involves utilizing the knowledge that the agent has already acquired to maximize rewards. This means that the agent will choose actions that it believes will yield the highest rewards based on its existing knowledge. Essentially, exploitation is about leveraging known information to make the best decision right now, rather than risking a lower reward by trying something new.

Option B accurately captures this distinction by stating that exploration seeks new options, while exploitation focuses on known high-reward actions. It emphasizes the need for a balanced approach in reinforcement learning, where an agent must explore new possibilities while also taking advantage of what it already knows to optimize performance.