Exploration and exploitation are two key concepts in reinforcement learning (RL) that guide an agent's decision-making process. Exploration refers to the agent trying out new actions to discover potentially better strategies or rewards, while exploitation involves the agent using known actions that have already led to high rewards in the past.
Exploration is important because it allows the agent to gather more information about the environment and avoid getting stuck in suboptimal solutions. On the other hand, exploitation leverages the agent's existing knowledge to maximize immediate rewards. In practice, agents must strike a balance between exploring new actions and exploiting the best-known actions.
An example would be in a navigation task where an agent may choose between exploring a new path (exploration) or sticking to a previously successful path (exploitation). Balancing exploration and exploitation is crucial for ensuring that the agent doesn't miss better strategies or settle on a suboptimal one prematurely.