10 Reinforcement learning with Q-learning

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.

The inspiration for reinforcement learning

Problems applicable to reinforcement learning

The life cycle of reinforcement learning

Simulation and data: Make the environment come alive

Training with the simulation using Q-learning

Testing with the simulation and Q-table

Measuring the performance of training

Model-free and model-based learning

Deep learning approaches to reinforcement learning

Use cases for reinforcement learning

Robotics

Recommendation engines

Financial trading

Game playing

Summary of reinforcement learning