Deep Reinforcement Learning Hands-On

If you want to learn how to create intelligent agents capable of mastering complex environments, Deep Reinforcement Learning Hands-On is an essential resource. This book provides a developer-oriented introduction to reinforcement learning (RL), covering fundamental concepts and advanced algorithms. You'll explore practical implementation techniques and gain the skills to solve real-world problems using RL.

What this Book will help me do

• Understand and implement foundational RL concepts, such as Markov decision processes and Q-learning.
• Apply deep RL algorithms like DQN, TRPO, PPO, and AlphaGo Zero in practical contexts.
• Develop agents that solve Atari arcade and board games like Connect4 through self-learning.
• Explore real-world applications, from designing stock trading agents to building conversational chatbots.
• Utilize frameworks like PyTorch and OpenAI Gym for building and testing RL models.

Author(s)

Maxim Lapan is a seasoned author and practitioner in the fields of artificial intelligence and machine learning. He has extensive experience working with deep learning and reinforcement learning technologies. Maxim brings a perfect blend of academic understanding and practical insights, offering readers a hands-on approach to complex topics in RL.

Who is it for?

This book is ideal for developers and machine learning practitioners interested in mastering reinforcement learning. Readers should have some familiarity with Python and deep learning fundamentals. It is particularly suitable for those aiming to apply RL to practical projects and real-world problems. The content caters to both beginners in reinforcement learning and those looking to advance their skills in the domain.