Q Learning Algorithm C# Implementation Tutorial

Tutorial and Source Code can be found at:
https://code-ai.mk/how-to-implement-q...

Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. It does not require a model (hence the connotation "model-free") of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.

Reinforcement Learning is a process by which organisms acquire information about stimuli, actions, and context that predict positive outcomes. It can also modify behavior when a novel reward occurs, or the outcome is better than what we were expecting.

In other words, we give reward as a stimulus to a human or some other animal to alter its behavior. The reward typically serves as reinforcer.

We have all done it. Reinforcement Learning is teaching your dog to do tricks. You provide treats as a reward, if your pet performs the trick, otherwise you punish him by taking away the goodies. So, if you are a pet owner, you have probably trained and rewarded your pet after every positive behavior.

As humans, we also have experienced the same. Positive outcomes come with a reward. However, negative outcomes are punishable.

This tutorial focuses on the Q Learning algorithm and it's implementation. In a later tutorials we will dig deep into the mathematics and more advanced visualizations of the problem.