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BL
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| Record Number
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856581
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| Main Entry
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DUTTA, SAYON.
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| Title & Author
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Reinforcement Learning with TensorFlow : : a beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym.
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| Publication Statement
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Birmingham :: Packt Publishing,, 2018.
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| Page. NO
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1 online resource (327 pages)
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| ISBN
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1788830717
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: 1788835727
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: 9781788830713
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: 9781788835725
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| Notes
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On-policy and off-policy learning.
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| Contents
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Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Deep Learning -- Architectures and Frameworks; Deep learning; Activation functions for deep learning; The sigmoid function; The tanh function; The softmax function; The rectified linear unit function; How to choose the right activation function; Logistic regression as a neural network; Notation; Objective; The cost function; The gradient descent algorithm; The computational graph; Steps to solve logistic regression using gradient descent; What is xavier initialization?
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Asynchronous advantage actor-criticIntroduction to TensorFlow and OpenAI Gym; Basic computations in TensorFlow; An introduction to OpenAI Gym; The pioneers and breakthroughs in reinforcement learning; David Silver; Pieter Abbeel; Google DeepMind; The AlphaGo program; Libratus; Summary; Chapter 2: Training Reinforcement Learning Agents Using OpenAI Gym; The OpenAI Gym; Understanding an OpenAI Gym environment; Programming an agent using an OpenAI Gym environment; Q-Learning; The Epsilon-Greedy approach; Using the Q-Network for real-world applications; Summary; Chapter 3: Markov Decision Process.
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Markov decision processesThe Markov property; The S state set; Actions; Transition model; Rewards; Policy; The sequence of rewards -- assumptions; The infinite horizons; Utility of sequences; The Bellman equations; Solving the Bellman equation to find policies; An example of value iteration using the Bellman equation; Policy iteration; Partially observable Markov decision processes; State estimation; Value iteration in POMDPs; Training the FrozenLake-v0 environment using MDP; Summary; Chapter 4: Policy Gradients; The policy optimization method; Why policy optimization methods?
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Why do we use xavier initialization?The neural network model; Recurrent neural networks; Long Short Term Memory Networks; Convolutional neural networks; The LeNet-5 convolutional neural network; The AlexNet model; The VGG-Net model; The Inception model; Limitations of deep learning; The vanishing gradient problem; The exploding gradient problem; Overcoming the limitations of deep learning; Reinforcement learning; Basic terminologies and conventions; Optimality criteria; The value function for optimality; The policy model for optimality; The Q-learning approach to reinforcement learning.
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Why stochastic policy?Example 1 -- rock, paper, scissors; Example 2 -- state aliased grid-world; Policy objective functions; Policy Gradient Theorem; Temporal difference rule; TD(1) rule; TD(0) rule; TD() rule; Policy gradients; The Monte Carlo policy gradient; Actor-critic algorithms; Using a baseline to reduce variance; Vanilla policy gradient; Agent learning pong using policy gradients; Summary; Chapter 5: Q-Learning and Deep Q-Networks; Why reinforcement learning?; Model based learning and model free learning; Monte Carlo learning; Temporal difference learning.
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| Abstract
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Reinforcement learning allows you to develop intelligent, self-learning systems. This book shows you how to put the concepts of Reinforcement Learning to train efficient models. You will use popular reinforcement learning algorithms to implement use-cases in image processing and NLP, by combining the power of TensorFlow and OpenAI Gym.
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| Subject
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Reinforcement learning.
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| Subject
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Reinforcement learning.
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| Dewey Classification
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006.31
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| LC Classification
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Q325.6.D888 2018eb
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