4. October 2020
This is a short post to describe my practical introduction to Reinforcement Learning (RL), where I trained a simple agent
to play the classic Atari game Pong via a Deep Q-Network.
In English, this means we teach a novice computer to play the
classic paddle game by allowing it to observe what happens when it performs various movements at different times and
stages of gameplay (against the same, fairly strong opponent). Then, after making a sequence of movement
choices, our agent either gets a point (reward of +1) or loses one (reward of -1). After a lot of trial and error, the
agent will have observed enough situations to learn what is a good move to make at a given moment in the game.