Reinforcement Learning
This chapter introduces Reinforcement Learning (RL)—the least explored and yet most promising learning paradigm. Reinforcement learning is very different from the supervised and unsupervised learning models we covered in earlier chapters. Starting from a clean slate (that is, having no prior information), the RL agent can go through multiple stages of trial and error, and learn to achieve a goal, all the while the only input being the feedback from the environment. The 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 RL is and its lingo
- Learn how to use the OpenAI Gym interface
- Applications of RL
- Deep Q-Networks
- Policy...
