Reinforcement Learning: A Comprehensive Guide to Understanding and Implementation
Introduction
In today’s world of rapid technological advancements, the question of “how to learn a new skill” has become a fundamental research problem. The willingness to solve this problem is evident, as it holds the key to creating machines that can perform original work, thereby ushering in the era of true artificial intelligence. Although we do not have a complete answer to this problem yet, there are certain aspects that are clear. One of the most fundamental theories and concepts of all learning and development theories of intelligence is interactive learning, which is the basis of all learning.
Reinforcement Learning
Reinforcement learning is a type of machine learning algorithm that is based on the idea of interacting with the environment and receiving feedback in the form of rewards or penalties. This type of learning is considered to be one of the most promising approaches to achieving strong artificial intelligence. The research on reinforcement learning is growing rapidly, resulting in a variety of learning algorithms for different applications. Therefore, understanding reinforcement learning has become increasingly important.
Understanding the Basics of Reinforcement Learning
If you are not familiar with reinforcement learning, we suggest that you first read our previous article on the topic and explore some of the open-source platforms available. If you are already familiar with the basics of reinforcement learning, then please continue to read this article. After reading this article, you will have a thorough understanding of the reinforcement learning process and its implementation.
Table of Contents
- Reinforcement Learning Forms of Problems
- Comparison with Other Machine Learning Methods
- Reinforcement Learning Framework to Solve Problems
- Reinforcement Learning Principles
- Increase the Complexity of Reinforcement Learning
- In-Depth Understanding of the Latest Developments in Reinforcement Learning
- Other Resources
Reinforcement Learning Forms of Problems
Reinforcement learning not only requires learning what to do but also learning how to take appropriate actions based on interaction with the environment. The final result of reinforcement learning is to maximize the return signal system. The learner does not know in advance what behavior to perform and needs to discover what kind of action can generate the greatest return. Let us explain this by using a simple example.
Learning to Walk
Imagine a child learning to walk. The first thing the child is concerned with is observing how people around them are walking. People around them use two legs, one step, and step by step in accordance with the order to move forward. The child seizes this concept and tries to imitate this process. However, soon the child understands that before walking, they must stand up. Standing for the child is a challenge, and they continue to fall but still decide to stand up. The real task is to start the child learning to walk, but learning to walk is easy to say, and actually doing it is not so easy.
Problem Example: “Walk Problem”
Let us use a specific form to express the above example. The problem is stated as “walk problem” where the child is an attempt to manipulate the agent environment (walking on the ground) by taking action (walk) and trying to move from one state (standing) to another state (walking). When the child completes the task of a sub-module (taking a few steps), the child will be rewarded (for example, some chocolate), but when the child cannot walk, they will not receive any chocolate (this is a negative feedback process).
Comparison with Other Machine Learning Methods
Reinforcement learning is a machine learning algorithm in a class. Let us compare the difference between reinforcement learning algorithms and other types of algorithms:
- Supervised Learning and Reinforcement Learning: In supervised learning, there is a “competent supervision” that possesses knowledge of the environment and shares this knowledge with the agent. However, creating a “competent supervision” is almost unrealistic. In reinforcement learning, there is a reward function that provides feedback to the agent.
- Unsupervised Learning and Reinforcement Learning: In unsupervised learning, there is a mapping between input and output, but in reinforcement learning, there is a process of mapping from input to output, and the process in unsupervised learning does not exist.
- Semi-Supervised Learning: Semi-supervised learning is essentially a combination of supervised learning and unsupervised learning. It is different from reinforcement learning but similar to supervised learning.
Reinforcement Learning Framework to Solve Problems
To understand the reinforcement learning process of solving problems, let us explain reinforcement learning problems through a classic example - MAB. We need to understand the basic issues of exploration and exploitation and define the framework for solving reinforcement learning problems.
Markov Decision Process
The Markov decision process is a mathematical framework to solve the problem in reinforcement learning scenarios. It is designed to:
- State set: S
- Set of actions: A
- Reward function: R
- Strategy: π
- Value: V
To transition from the start state to the end state (S), we must take certain actions (A). Every time after we take action, we will get some reward as an incentive. The nature of the acquired reward (positive or negative incentive award) is determined by our actions.
Traveling Salesman Problem
Let us look at another example to illustrate the traveling salesman problem (TSP). The task is to reach from point A to point F at the lowest possible cost. Each edge between two letters takes place between the two, and if this value is negative, it means that using this road will give you some reward. We define the value (Value) as the total value when using the strategy selected through the whole journey.
Conclusion
Reinforcement learning is a type of machine learning algorithm that is based on the idea of interacting with the environment and receiving feedback in the form of rewards or penalties. It is considered to be one of the most promising approaches to achieving strong artificial intelligence. In this article, we have explained the basics of reinforcement learning, its forms of problems, comparison with other machine learning methods, reinforcement learning framework to solve problems, reinforcement learning principles, increase the complexity of reinforcement learning, and in-depth understanding of the latest developments in reinforcement learning.
Other Resources
- Reinforcement Learning Forms of Problems: This article provides a comprehensive overview of reinforcement learning forms of problems.
- Comparison with Other Machine Learning Methods: This article compares the difference between reinforcement learning algorithms and other types of algorithms.
- Reinforcement Learning Framework to Solve Problems: This article explains reinforcement learning problems through a classic example - MAB.
- Reinforcement Learning Principles: This article provides an in-depth understanding of the principles of reinforcement learning.
- Increase the Complexity of Reinforcement Learning: This article discusses the increase in complexity of reinforcement learning.
- In-Depth Understanding of the Latest Developments in Reinforcement Learning: This article provides an in-depth understanding of the latest developments in reinforcement learning.