Throughout this book, we approach two main types of model-free algorithms: the ones based on the gradient of the policy, and the ones based on the value function. From the first family, we saw REINFORCE, actor-critic, PPO, and TRPO. From the second, we saw Q-learning, SARSA, and DQN. As well as the way in which the two families learn a policy (that is, policy gradient algorithms use stochastic gradient ascent toward the steepest increment on the estimated return, and value-based algorithms learn an action value for each state-action to then build a policy), there are key differences that let us prefer one family over the other. These are the on-policy or off-policy nature of the algorithms, and their predisposition to manage large action spaces. We already discussed the differences between on-policy and off-policy in the previous...
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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