In the last few chapters, we have learned how Deep Q learning works by approximating the q function with a neural network. Following this, we have seen various improvements to Deep Q Network (DQN) such as Double Q learning, dueling network architectures, and the Deep Recurrent Q Network. We have seen how DQN makes use of a replay buffer to store the agent's experience and trains the network with the mini-batch of samples from the buffer. We have also implemented DQNs for playing Atari games and a Deep Recurrent Q Network (DRQN) for playing the Doom game. In this chapter, let's get into the detailed implementation of a dueling DQN, which is essentially the same as a regular DQN, except the final fully connected layer will be broken down into two streams, namely a value stream and an advantage stream, and these two streams...
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