Now that we have learned the world's simplest game, let's try learning something a bit more dynamic. The cart pole task is a classic reinforcement learning problem. The agent must control a cart, on which is balanced a pole, attached to the cart via a joint. At every step, the agent can choose to move the cart left or right, and it receives a reward of 1 every time step that the pole is balanced. If the pole ever deviates by more than 15 degrees from upright, then the game ends:
Figure 5: The cart pole task
To run the cart pole task, we will use OpenAIGym, an open source project set up in 2015, which gives a way to run reinforcement learning agents against a range of environments in a consistent way. At the time of writing, OpenAIGym has support for running a whole range of Atari games and even some more complex games, such as doom, with minimum setup. It can be installed using pip by running this:
pip install gym[all]
Running cart pole in Python can be done as follows:
import...
