Basically, PPO is a way for computers to learn how to do stuff by playing games and figuring out what actions lead to the best rewards.

So let’s say you have this game where you control a little guy who has to navigate through an obstacle course without hitting any walls or falling into pits. The goal is to get him from point A to point B as quickly as possible, and PPO can help us figure out the best way to do that by learning which actions lead to the highest rewards (i.e., getting to point B in the shortest amount of time).

Here’s how it works: first, we start with a random policy this is just a fancy term for saying “we don’t know what actions are best yet.” We then collect some data by playing the game and recording all the different states (i.e., where our little guy is in the obstacle course) and the corresponding rewards that he gets from taking certain actions.

Next, we use a fancy algorithm called stochastic gradient descent to update the weights of our policy based on this data. This basically means that we’re trying to find the best set of weights (i.e., numbers) for our policy so that it can learn how to take the most rewarding actions in each state.

The cool thing about PPO is that it allows us to do multiple epochs of training on the same data, which makes it much more sample-efficient than other algorithms out there (i.e., we don’t need as many samples to learn how to play the game well). This is because each time we update our policy using stochastic gradient descent, we’re essentially making small adjustments to the weights based on what worked best in previous iterations this helps us converge faster and more accurately.

PPO: a fancy algorithm that can help computers learn how to play games by figuring out which actions lead to the highest rewards. And if you’re curious about how it works, just remember: stochastic gradient descent + multiple epochs = sample efficiency and faster learning times.