#### 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.
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
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# Chapter 7

1. In neurons, we introduce non-linearity to the result, z, by applying a function f() called the activation or transfer function. Refer section Artificial neurons.
2. Activation functions are used for introducing nonlinearity.
3. We calculate the gradient of the cost function with respect to the weights to minimize the error.
4. RNN predicts the output not only based on the current input but also on the previous hidden state.
5. While backpropagating the network if the gradient value becomes smaller and smaller it is called vanishing gradient problem if the gradient value becomes bigger then it is exploding gradient problem.
6. Gates are special structures in LSTM used to decide what information to keep, discard and update.
7. The pooling layer is used to reduce the dimensions of the feature maps and keeps only necessary details so that the amount of computation can be reduced.
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