10 Reinforcement learning with Q-learning
This chapter covers
- Understanding the inspiration for reinforcement learning
- Identifying problems to solve with reinforcement learning
- Designing and implementing a reinforcement learning algorithm
- Understanding reinforcement learning approaches
10.1 What is reinforcement learning?
Reinforcement Learning (RL) is an area of machine learning inspired by behavioral psychology. The concept of reinforcement learning is based on cumulative rewards or penalties for the actions that are taken by an agent in a dynamic environment. Think about a young dog growing up. The dog is the agent in an environment that is our home. When we want the dog to sit, we usually say, “Sit.” The dog doesn’t understand English, so we might nudge it by lightly pushing down on its hindquarters. After it sits, we usually pet the dog or give it a treat. This process will need to be repeated several times, but after some time, we have positively reinforced the idea of sitting. The trigger in the environment is saying “Sit”; the behavior learned is sitting; and the reward is pets or treats.