The exploration-exploitation tradeoff refers to the balance an agent must strike between exploring new actions and exploiting known actions that lead to higher rewards.
Exploration involves taking actions that may not immediately lead to high rewards but can potentially uncover more rewarding strategies in the long run. This helps the agent learn more about the environment and find better policies. Exploitation, on the other hand, means choosing actions that are known to yield higher rewards based on past experience.
Balancing the two is crucial: too much exploration can slow down learning as the agent might not take advantage of the optimal strategies it has already discovered, while too much exploitation can lead to suboptimal behavior since the agent might miss better options by sticking to familiar actions. Techniques like epsilon-greedy, which starts with a high exploration rate and gradually shifts toward more exploitation, help manage this balance.