You want your computer to learn how to do this on its own, without any human input or guidance. That’s where deep reinforcement learning comes in!

First, we need to create an environment for our agent to interact with. This could be a virtual maze that the agent can move around in and collect treasure from. We also need to define some rules for how the game works like what actions are available (move left, right, up or down) and what rewards the agent gets for completing certain tasks (like collecting treasure).

Next, we train our agent using a process called trial-and-error learning. The agent starts in a random position within the maze and takes an action based on its current state (which includes things like where it is, how much treasure it has collected so far, etc.). It then receives feedback from the environment either a reward or a penalty depending on whether it took a good or bad action.

Over time, our agent learns which actions are most likely to result in rewards and starts making better decisions as a result. This is where deep learning comes into play we use neural networks (which are essentially fancy math equations) to help the agent make more accurate predictions about what actions will lead to the best outcomes.

Here’s an example of how this might work in practice: let’s say our agent starts off by taking a random action and moving left. It receives feedback from the environment (which could be something like “you collected some treasure!” or “oops, you ran into a wall!”) based on whether that action was successful or not.

Over time, as our agent continues to interact with the maze and collect more rewards for taking good actions, it starts to learn which actions are most likely to lead to success. Eventually, it becomes so skilled at navigating through the maze that it can complete the task without any human input or guidance!

It’s a powerful tool for teaching computers how to solve complex problems on their own, and it has applications in everything from gaming to robotics.