- Reinforcement learning (RL) is a branch of machine learning where the learning occurs via interacting with an environment.
- RL works by train and error method, unlike other ML paradigms.
- Agents are the software programs that make intelligent decisions and they are basically learners in RL.
- Policy function specifies what action to take in each state and value function specifies the value of each state.
- In model-based agent use the previous experience whereas in model-free learning there won't be any previous experience.
- Deterministic, stochastic, fully observable, partially observable, discrete continuous, episodic and non-episodic.
- OpenAI Universe provides rich environments for training RL agents.
- Refer section Applications of RL.
Hands-On Reinforcement Learning with Python
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Hands-On Reinforcement Learning with Python
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Overview of this book
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.
The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.
By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
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Current Title:
Hands-On Reinforcement Learning with Python
Table of Contents (16 chapters)
Preface
Free Chapter
Introduction to Reinforcement Learning
Getting Started with OpenAI and TensorFlow
The Markov Decision Process and Dynamic Programming
Gaming with Monte Carlo Methods
Temporal Difference Learning
Multi-Armed Bandit Problem
Deep Learning Fundamentals
Atari Games with Deep Q Network
Playing Doom with a Deep Recurrent Q Network
The Asynchronous Advantage Actor Critic Network
Policy Gradients and Optimization
Capstone Project – Car Racing Using DQN
Recent Advancements and Next Steps
Assessments
Other Books You May Enjoy
Customer Reviews