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Reinforcement Learning
This chapter introduces reinforcement learning (RL)—the least explored and yet
most promising learning paradigm. Reinforcement learning is very different from
both supervised and unsupervised learning models we have done in earlier chapters.
Starting from a clean slate (that is, having no prior information), the RL agent can
go through multiple stages of hit and trials, and learn to achieve a goal, all the while
the only input being the feedback from the environment. The latest research in RL
by OpenAI seems to suggest that continuous competition can be a cause for the
evolution of intelligence. Many deep learning practitioners believe that RL will play
an important role in the big AI dream: Artificial General Intelligence (AGI). This
chapter will delve into different RL algorithms, the following topics will be covered:
• What is RL and its lingo
• Learn how to use OpenAI Gym interface
• Deep Q-Networks
• Policy gradients
Introduction
What is common between a baby learning to walk, birds learning to fly, or an RL
agent learning to play an Atari game? Well, all three involve:
• Trial and error: The child (or the bird) tries various ways, fails many times,
and succeeds in some ways before it can really stand (or fly). The RL Agent
plays many games, winning some and losing many, before it can become
reliably successful.
• Goal: The child has the goal to stand, the bird to fly, and the RL agent has the
goal to win the game.
• Interaction with the environment: The only feedback they have is from their
environment.
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