In the Mountain Car Gym environment, a car is on a one-dimensional track, positioned between two mountains. The goal is to drive the car up the mountain on the right; however, the car's engine is not strong enough to drive up the mountain even at the maximum speed. Therefore, the only way to succeed is to drive back and forth to build up momentum. In short, the Mountain Car problem is to get an under-powered car to the top of a hill.
Before you implement your agent algorithm, it will help tremendously to understand the environment, the problem, and the state and action spaces. How do we find out the state and action spaces of the Mountain Car environment in the Gym? Well, we already know how to do ...
