Reinforcement Learning : An Introduction
#Learn RL in a week
I would like to recommend studying reinforcement learning , if you want to learn more about our life, our world that we believe we exist.., it is a subfield of machine learning. Understanding the terms and concepts in machine learning and deep learning can change your perspective on human life, Earth, and the universe. As a physics graduate, I can say that it offers a different perspective than that of a physics enthusiast and is more philosophical.
After studying machine learning, I find myself facing some issues when interacting with small babies and children. I am unable to entertain them or even smile at them without my mind wandering to the basic principles of machine learning. Our childhood experiences play a significant role in shaping our future, and we learn from our surroundings and the people around us.
There are different types of learning methods in machine learning, including supervised, unsupervised, reinforcement, and semi-supervised learning. In this discussion, we will focus on reinforcement learning.
Here are some real-life examples that illustrate the key elements of reinforcement learning:
Agent: A self-driving car is an example of an agent that interacts with the environment by taking actions such as accelerating, braking, and turning.
Environment: The environment for a self-driving car includes the road, other vehicles, pedestrians, and traffic lights.
State: The state of the self-driving car could include its current speed, location, and orientation on the road.
Action: An action for the self-driving car could be to accelerate or to brake, based on the state of the environment.
As you can see below, Another important thing is rewards,
Reward: The reward for the self-driving car could be a positive value for reaching its destination quickly and safely and a negative value for causing an accident.
Policy: A policy for self-driving cars could be a set of rules that determine how the car should act in certain situations. For example, the policy might specify that the car should always stop at a red traffic light.
Value Function: A value function for the self-driving car could estimate the expected reward the car will receive in the future based on its current state and policy.
Model: A model for the self-driving car could simulate the consequences of taking a particular action in a particular state, such as accelerating in heavy traffic.
So, there you have it — a glimpse into the intriguing world of reinforcement learning, where machines learn from their actions, much like we do in our daily lives. It’s a field filled with exciting possibilities and a deep connection to how we interact with the world around us.
