An Introduction to Reinforcement Learning: A comprehensive and informative guide

by Locally Sourced

**An Introduction to Reinforcement Learning**

Reinforcement learning is a powerful technique that allows machines to learn from their own experiences and improve their performance without explicitly being programmed. In this book, **An Introduction to Reinforcement Learning**, we provide a comprehensive introduction to the field of reinforcement learning.

This book is designed for both beginners and experienced practitioners who want to understand the fundamental concepts and principles behind reinforcement learning. With clear explanations and illustrative examples, we guide you through the foundational concepts, algorithms, and applications of reinforcement learning.

Here's what you'll find inside this

**Table of

**Chapter 1: Introduction to Reinforcement Learning**
- What is reinforcement learning?
- History and applications of reinforcement learning
- Markov decision processes (MDPs)
- Key components of reinforcement learning

**Chapter 2: Modeling Agents and Environments**
- Agent and environment interaction
- Observations, states, and actions
- Rewards and discount factors
- Markov property and dynamic programming

**Chapter 3: Finite Markov Decision Processes**
- Policy evaluation and improvement
- Value iteration and policy iteration
- Solving finite MDPs with algorithms

**Chapter 4: Deep Q-Networks and Q-Learning**
- Neural networks and deep learning
- Q-learning and function approximation
- Implementing DQNs with TensorFlow

**Chapter 5: Policy Gradient Methods**
- Policy parameterization
- Policy gradient theorem
- REINFORCE algorithm and its variants

**Chapter 6: Monte Carlo Methods**
- Learning from complete sequences
- First-visit and every-visit MC methods
- Incremental implementation and convergence analysis

**Chapter 7: Temporal-Difference Methods**
- SARSA algorithm and its variants
- n-step bootstrapping
- Eligibility traces and TD(λ)

**Chapter 8: Exploration and Exploitation**
- Exploration-exploitation trade-off
- ϵ-greedy and greedy policies
- Upper confidence bound (UCB) and Thompson sampling

**Chapter 9: Function Approximation**
- Linear function approximation
- Artificial Neural Networks (ANNs)
- Value function approximation

**Chapter 10: Policy Search Methods**
- Direct policy search
- Cross-entropy method
- Gaussian policies and probabilistic policy search

**Chapter 11: Reinforcement Learning in Robotics**
- Robotic control problems
- Learning to control robots
- Application of reinforcement learning in robotics

Whether you're interested in building your own intelligent agent or you simply want to gain a deeper understanding of reinforcement learning, **An Introduction to Reinforcement Learning** is the perfect guide. With its comprehensive coverage and practical examples, it will equip you with the knowledge and skills to tackle real-world problems using reinforcement learning techniques.

Get started on your journey to mastery in reinforcement learning with **An Introduction to Reinforcement Learning** today!

77 pages, Kindle Edition

Published August 29, 2023