Crash-Introduction to Reinforcement Learning

This keynote presentation has been prepared for a technical podcast and is meant to be a rapid (but relatively comprehensive) introduction to the essential concepts of Reinforcement Learning.

Table of Contents

2 Minutes Read

Summary note on Reinforcement Learning

Reinforcement Learning is a type of machine learning in which an agent learns to make decisions by interacting with its environment. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn the optimal strategy for maximizing long-term rewards.

The main concepts of Reinforcement Learning include the agent, the decision maker, the environment the agent interacts with, the actions the agent can take, the rewards the agent receives based on their actions, and policy, which defines the agent’s strategy to make decisions.

Fundament concepts of Reinforcement Learning — The fundamental structure of Reinforcement Learning shows the following: the agent, the environment, the policy algorithm, and the action of feedback in determining the learning step and action policy improvement.

Reinforcement Learning algorithms enable the agent to learn from experience, gradually improving its decision-making capabilities over time. Some popular learning algorithms include Q-learning, Deep Q Networks (DQN), and Policy Gradient methods.

These algorithms have applications in various fields, such as robotics, games, finance, and healthcare, where autonomous decision-making is crucial. By understanding the fundamental concepts of this learning approach, developers can create intelligent systems that can learn and adapt to complex environments.

Reference

Bonaccorso G., Mastering Machine Learning Algorithms (2nd Edition), Packt Publishing, 2020

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Crash-Introduction to Reinforcement Learning

Summary note on Reinforcement Learning

Reference

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