Reinforcement Learning Algorithms with Python
With this book, you will understand the core concepts and techniques of reinforcement learning. You will take a look into each RL algorithm and will develop your own self-learning algorithms and models. You will optimize the algorithms for better precision, use high-speed actions and lower the risk of anomalies in your applications.
Offered by
Difficulty Level
Intermediate
Completion Time
12h12m
Language
English
About Book
Who Is This Book For?
If you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. You’ll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary.
Reinforcement Learning Algorithms with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h12m total length
The Landscape of Reinforcement Learning
Implementing RL Cycle and OpenAI Gym
Solving Problems with Dynamic Programming
Q learning and SARSA Applications
Deep Q-Network
Learning Stochastic and DDPG optimization
TRPO and PPO implementation
DDPG and TD3 Applications
Model-Based RL
Imitation Learning with the DAgger Algorithm
Understanding Black-Box Optimization Algorithms
Developing the ESBAS Algorithm
Practical Implementation for Resolving RL Challenges
Related Resources
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