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E-raamat: Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python

  • Formaat: PDF+DRM
  • Ilmumisaeg: 08-Dec-2023
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484296066
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 08-Dec-2023
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484296066
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Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology.





Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO).





This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques.





With a focus on explaining algorithms and the intuition behind them, The Art of Reinforcement Learning includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students.





What You Will Learn













Grasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approaches Model problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learning Utilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methods Understand the architecture and advantages of distributed reinforcement learning Master the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agents Explore the AlphaZero algorithm and how it was able to beat professional Go players





























 Who This Book Is For





Machine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.
I Foundation 1
1 Introduction 3
1.1 AI Breakthrough in Games . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 What is Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . 9
1.3 Agent-Environment in Reinforcement Learning . . . . . . . . . . . . . 10
1.4 Examples of Reinforcement Learning . . . . . . . . . . . . . . . . . . 15
1.5 Common terms in Reinforcement Learning . . . . . . . . . . . . . . . 17
1.6 Why study Reinforcement Learning . . . . . . . . . . . . . . . . . . . 20
1.7 The Challenges in Reinforcement Learning . . . . . . . . . . . . . . . 23

2 Markov Decision Processes 29
2.1 Overview of MDP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2 Model Reinforcement Learning Problem using MDP . . . . . . . . . . 31
2.3 Markov Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.4 Markov Reward Process . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.5 Markov Decision Process . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.6 Alternative Bellman Equations for Value Functions . . . . . . . . . . 54
2.7 Optimal Policy and Optimal Value Functions . . . . . . . . . . . . . 56

3 Dynamic Programming 61
3.1 Policy Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.2 Policy Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3 Policy Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.4 General Policy Iteration . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.5 Value Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4 Monte Carlo Methods 79
4.1 Monte Carlo Policy Evaluation . . . . . . . . . . . . . . . . . . . . . 80
4.2 Incremental Update . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
iii
Contents iv
4.3 Exploration vs. Exploitation . . . . . . . . . . . . . . . . . . . . . . . 89
4.4 Monte Carlo Control (Policy Improvement) . . . . . . . . . . . . . . . 93

5 Temporal Di?erence Learning 99
5.1 Temporal Di?erence Learning . . . . . . . . . . . . . . . . . . . . . . 99
5.2 Temporal Di?erence Policy Evaluation . . . . . . . . . . . . . . . . . 100
5.3 Simplified ?-greedy policy for Exploration . . . . . . . . . . . . . . . . 107
5.4 TD Control - SARSA . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
5.5 On-policy vs. O?-policy . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.6 Q-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.7 Double Q-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.8 N-step Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

II Value Function Approximation 143
6 Linear Value Function Approximation 145
6.1 The Challenge of Large-scale MDPs . . . . . . . . . . . . . . . . . . . 145
6.2 Value Function Approximation . . . . . . . . . . . . . . . . . . . . . . 148
6.3 Stochastic Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . 152
6.4 Linear Value Function Approximation . . . . . . . . . . . . . . . . . . 159

7 Nonlinear Value Function Approximation 171
7.1 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
7.2 Training Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . 183
7.3 Policy Evaluation with Neural Networks . . . . . . . . . . . . . . . . 188
7.4 Naive Deep Q-learning . . . . . . . . . . . . . . . . . . . . . . . . . . 190
7.5 Deep Q-learning with Experience Replay and Target Network . . . . 191
7.6 DQN for Atari Games . . . . . . . . . . . . . . . . . . . . . . . . . . 200

8 Improvement to DQN 211
8.1 DQN with Double Q-learning . . . . . . . . . . . . . . . . . . . . . . 211
8.2 Prioritized Experience Replay . . . . . . . . . . . . . . . . . . . . . . 214
8.3 Advantage function and Dueling Network Architecture . . . . . . . . 219

III Policy Approximation 225
9 Policy Gradient Methods 227
9.1 Policy-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
9.2 Policy Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
9.3 REINFORCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
9.4 REINFORCE with Baseline . . . . . . . . . . . . . . . . . . . . . . . 239
9.5 Actor-Critic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
9.6 Using Entropy to Encourage Exploration . . . . . . . . . . . . . . . . 249

10 Problems with Continuous Action Space 255
10.1 The Challenges of Problems with Action Space Problems . . . . . . . 256
10.2 MuJoCo Environments . . . . . . . . . . . . . . . . . . . . . . . . . . 257
10.3 Policy Gradient for Problems with Continuous Action Space . . . . . 260

11 Advanced Policy Gradient Methods 267
11.1 Problems with the standard Policy Gradients methods . . . . . . . . 267
11.2 Policy Performance Bounds . . . . . . . . . . . . . . . . . . . . . . . 270
11.3 Proximal Policy Optimization . . . . . . . . . . . . . . . . . . . . . . 277

IV Advanced Topics 287
12 Distributed Reinforcement Learning 289
12.1 Why use Distributed Reinforcement Learning . . . . . . . . . . . . . 289
12.2 General Distributed Reinforcement Learning Architecture . . . . . . . 290
12.3 Data Parallelism for Distributed Reinforcement Learning . . . . . . . 298

13 Curiosity-Driven Exploration 301
13.1 Hard-to-explore problems vs. Sparse Reward problems . . . . . . . . 302
13.2 Curiosity-Driven Exploration . . . . . . . . . . . . . . . . . . . . . . . 303
13.3 Random Network Distillation . . . . . . . . . . . . . . . . . . . . . . 305

14 Planning with a Model - AlphaZero 317
14.1 Why We Need to Plan in Reinforcement Learning . . . . . . . . . . . 317
14.2 Monte Carlo Tree Search . . . . . . . . . . . . . . . . . . . . . . . . . 320
14.3 AlphaZero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
Michael Hu is a skilled software engineer with over a decade of experience in designing and implementing enterprise-level applications. He's a passionate coder who loves to delve into the world of mathematics and has a keen interest in cutting-edge technologies like machine learning and deep learning, with a particular interest in deep reinforcement learning. He has build various open-source projects on Github, which closely mimic the state-of-the-art reinforcement learning algorithms developed by DeepMind, such as AlphaZero, MuZero, and Agent57. Fluent in both English and Chinese, Michael currently resides in the bustling city of Shanghai, China.
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