Hey there!

Mountain Car (MC) is a classic Reinforcement Learning (RL) problem. It was briefly shown in a video I was watching, so I figured I’d give it a shot.

Hey there!

Mountain Car (MC) is a classic Reinforcement Learning (RL) problem. It was briefly shown in a video I was watching, so I figured I’d give it a shot.

So last time, I solved the egg drop puzzle in a few ways. One of them was using a recent learn, Markov Decision Processes (MDP). It worked, which got me really stoked about them, because it was such a cool new method to me.

However, it’s kind of a baby process that’s mostly used as a basis to learn about more advanced techniques. In that solution to the problem, I defined the reward matrix and the transition probability matrix , and then used them explicitly to iteratively solve for the value function v and the policy p. This works, but isn’t very useful for the real world, because in practice you don’t *know* and , you just get to try stuff and learn the best strategy through experience. So the real challenge would be letting my program try a bunch of actual egg drops, and have it learn the value function and policy from them.