Reinforcement Learning (RL) is an approach wherein an agent learns to make sequential decisions by interacting with an environment. The objective is for the agent to maximize the cumulative reward it receives over time. The agent goes through this process by repeatedly evaluating the consequences of its actions, trying to select actions that lead to better outcomes.
To do this, we will use Gym, an platform for developing and comparing reinforcement learning algorithms. Gym provides an interface for interacting with different environments, it accepts actions from agents and plays them out in an environment, providing rewards.