# The Exploration/Exploitation trade-off [[exp-exp-tradeoff]]

Finally, before looking at the different methods to solve Reinforcement Learning problems, we must cover one more very important topic: *the exploration/exploitation trade-off.*

- *Exploration* is exploring the environment by trying random actions in order to **find more information about the environment.**
- *Exploitation* is **exploiting known information to maximize the reward.**

Remember, the goal of our RL agent is to maximize the expected cumulative reward. However, **we can fall into a common trap**.

Let’s take an example:

In this game, our mouse can have an **infinite amount of small cheese** (+1 each). But at the top of the maze, there is a gigantic sum of cheese (+1000).

However, if we only focus on exploitation, our agent will never reach the gigantic sum of cheese. Instead, it will only exploit **the nearest source of rewards,** even if this source is small (exploitation).

But if our agent does a little bit of exploration, it can **discover the big reward** (the pile of big cheese).

This is what we call the exploration/exploitation trade-off. We need to balance how much we **explore the environment** and how much we **exploit what we know about the environment.**

Therefore, we must **define a rule that helps to handle this trade-off**. We’ll see the different ways to handle it in the future units.

If it’s still confusing, **think of a real problem: the choice of picking a restaurant:**

Source:  Berkley AI Course

- *Exploitation*: You go to the same one that you know is good every day and **take the risk to miss another better restaurant.**
- *Exploration*: Try restaurants you never went to before, with the risk of having a bad experience **but the probable opportunity of a fantastic experience.**

To recap: