Explain the exploration-exploitation dilemma in reinforcement learning.

The exploration-exploitation dilemma in reinforcement learning refers to the trade-off an agent faces between exploring the environment to discover new and potentially better actions and exploiting its current knowledge to maximize its immediate reward. Exploration involves trying out actions that the agent has not yet tried or that it does not have a good estimate of their value, while exploitation involves selecting the action that the agent believes will yield the highest reward based on its current knowledge. If the agent only exploits, it might get stuck in a suboptimal policy because it never explores other potentially better actions. Conversely, if the agent only explores, it might not accumulate enough reward because it never consistently uses its knowledge to select the best action. For example, imagine an agent learning to play a video game. If the agent only exploits, it will always repeat the same sequence of actions that it has found to be successful so far, even if there might be other, more rewarding strategies that it has not yet discovered. If the agent only explores, it will randomly try out different actions without ever settling on a consistent strategy, preventing it from achieving high scores. Therefore, an effective reinforcement learning agent must strike a balance between exploration and exploitation, exploring enough to discover new and better actions, while also exploiting its current knowledge to maximize its reward. Common strategies for balancing exploration and exploitation include ε-greedy exploration, where the agent selects a random action with probability ε and the best action with probability 1-ε, and upper confidence bound (UCB) exploration, where the agent selects actions based on their estimated value plus an exploration bonus that encourages it to try out actions that have not been tried frequently.