AI agents balance exploration and exploitation by using strategies that allow them to gather new information while also making the best use of what they already know. Exploration involves trying out different actions to discover their potential rewards, while exploitation focuses on utilizing the actions known to yield the best outcomes based on existing data. The challenge lies in deciding when to explore new options and when to stick with known successful actions, which can be managed through various techniques.
One common method to balance these two aspects is called the epsilon-greedy strategy. In this approach, the AI agent mostly chooses the best-known action (exploitation), but with a small probability, it will select a random action (exploration). For example, if we set epsilon to 0.1, the agent will exploit its best option 90% of the time and explore a new action 10% of the time. This allows the agent to gather useful information about potentially better actions while still capitalizing on its learned experiences.
Another technique is Upper Confidence Bound (UCB), which accounts for uncertainty in the actions' rewards. In UCB, the agent evaluates the expected reward of each action, considering both the average known reward and a factor reflecting how much it has explored that action. This method encourages the agent to try actions that are less explored but may have a higher payoff. Such balancing techniques are fundamental in fields like reinforcement learning, where agents learn optimal policies over time through various interactions with their environment.