The game gives you points for each coin you grab, but it also takes away points if you get hurt.

Now let’s say you have two different strategies for playing the game: Strategy A and Strategy B. Strategy A is more aggressive and involves taking risks to collect coins faster, while Strategy B is more cautious and focuses on avoiding obstacles at all costs. Which strategy would be better in terms of getting the most points?

This is where reinforcement learning comes in! Reinforcement learning uses a reward system to determine which actions are best based on their outcomes. In our game example, collecting coins earns you points (reward), while getting hit by an enemy or obstacle takes away points (penalty). The goal of the PPO algorithm is to find the strategy that maximizes your total reward over time.

So how does it work? Let’s say we have a neural network that can predict which actions are best based on the current state of the game. We start by training this neural network using some initial set of strategies (like Strategy A and B). The PPO algorithm then takes over and starts optimizing these strategies to find the one with the highest total reward.

Here’s where the KL penalty comes in: it helps prevent the neural network from getting too greedy and always choosing the same action, even if that action isn’t necessarily the best choice for a given state. The KL penalty is essentially a way to measure how different two probability distributions are (in this case, the distribution of actions chosen by our initial set of strategies versus the distribution of actions chosen by the PPO algorithm).

The idea behind using a KL penalty is that we want our neural network to learn new strategies that are similar enough to our initial set of strategies, but also different enough to potentially find better rewards. By penalizing large differences between these distributions, we can encourage the PPO algorithm to explore more options and avoid getting stuck in local maxima (where a small change in strategy results in a big drop in reward).

The PPO algorithm with KL penalty is like having a super-smart coach that helps you find the best strategies for playing your favorite video games. And by using reinforcement learning, we can train these neural networks to learn new skills and adapt to changing environments over time.