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The Exploration/Exploitation trade-off

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:

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