Training an RL Agent to Solve a Classic Control Problem

In this section, we will learn how to train a reinforcement learning agent capable of solving a classic control problem named CartPole by building upon all the concepts explained previously. OpenAI Baselines will be leveraged and, following the steps highlighted in the previous section, we will use a custom fully connected network as a policy network, which is provided as input for the PPO algorithm.

Let's have a quick recap of the CartPole control problem. It is a classic control problem with a continuous four-dimensional observation space and a discrete two-dimensional action space. The observations that are recorded are the position and velocity of the cart along its line of movement, as well as the angle and angular velocity of the pole. The actions are the left/right movement of the cart along its rail. The reward is +1.0 for every step that does not result in a terminal state, which is the case if the pole moves...