Author: Zen and the Art of Computer Programming
1 Introduction
This is an introductory tutorial on reinforcement learning (Reinforcement Learning) and Q-learning algorithms. For readers who are new to reinforcement learning and do not have much relevant experience, being able to quickly understand its concepts and learn to apply its algorithms can greatly improve their own efficiency and problem-solving ability. The article will start with some basic concepts of reinforcement learning, gradually introduce the principle and specific operation steps of the algorithm, and finally give the specific Python code implementation. I hope that this article can help the majority of machine learning enthusiasts understand and use the value of reinforcement learning and Q-learning in practical applications.
2. What is reinforcement learning?
Reinforcement Learning (RL), also called Supervised Learning, is a sub-direction in the field of machine learning. RL aims to establish a dynamic system based on the environment, so that the agent (Agent) can continue to try and make mistakes in this system, obtain the maximum reward under the given input, and finally get a good strategy. This way of learning, just like our parents educating their children, is a methodology that enables the agent to learn and adapt to the environment through feedback, and finally learn to complete the task.
3. Basic concepts of reinforcement learning
First of all, it is necessary to clarify the four main components of reinforcement learning, including: Environment, Agent, State, and Action.
(1) Environment
Environment is a specific task or problem