Monte Carlo methods are a powerful way to learn directly by sampling from the environment, but they have a big drawback—they rely on the full trajectory. They have to wait until the end of the episode, and only then can they update the state values. Therefore, a crucial factor is knowing what happens when the trajectory has no end, or if it's very long. The answer is that it will produce terrifying results. A similar solution to this problem has already come up in DP algorithms, where the state values are updated at each step, without waiting until the end. Instead of using the complete return accumulated during the trajectory, it just uses the immediate reward and the estimate of the next state value. A visual example of this update is given in figure 4.2 and shows the parts involved in a single step of learning. This technique is called bootstrapping...
Reinforcement Learning Algorithms with Python
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Reinforcement Learning Algorithms with Python
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Overview of this book
Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents.
Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS.
By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community.
Table of Contents (19 chapters)
Preface
The Landscape of Reinforcement Learning
Implementing RL Cycle and OpenAI Gym
Solving Problems with Dynamic Programming
Section 2: Model-Free RL Algorithms
Q-Learning and SARSA Applications
Deep Q-Network
Learning Stochastic and PG Optimization
TRPO and PPO Implementation
DDPG and TD3 Applications
Section 3: Beyond Model-Free Algorithms and Improvements
Model-Based RL
Imitation Learning with the DAgger Algorithm
Understanding Black-Box Optimization Algorithms
Developing the ESBAS Algorithm
Practical Implementation for Resolving RL Challenges
Assessments
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