Learning the basics of reinforcement learning
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in a given state of an environment, maximizing the notion of cumulative reward.
To understand how RL helps, let’s consider a simple scenario. Imagine that you are playing chess against a computer. Let’s identify the different components involved:
- The computer is an agent that has learned/is learning how to play chess.
- The setup (rules) of the game constitutes the environment.
- As we make a move (take an action), the state of the board (the location of various pieces on the chessboard) changes.
- At the end of the game, depending on the result, the agent gets a reward. The objective of the agent is to maximize the reward.
If the machine (agent1) is playing against a human, the number of games that it can play is finite (depending on the number of games the human can play...
