Machine Learning for Developers

Decision elements

To understand the problem, let's situate ourselves in the problem solving environment and look at the main elements:

• The set of states
• The action to take is to go from one place to another
• The reward function is the value represented by the edge
• The policy is the way to complete the task
• A discount factor, which determines the importance of future rewards

The main difference with traditional forms of supervised and unsupervised learning is the time taken to calculate the reward, which in reinforcement learning is not instantaneous; it comes after a set of steps.

Thus, the next state depends on the current state and the decision maker's action, and the state is not dependent on all the previous states (it doesn't have memory), ...