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• Other Books You May Enjoy
• Chapter 13
• Chapter 12
• Chapter 11
• Chapter 10

• Leave a review - let other readers know what you think 更新時間：2021-06-18 19:12:48
• Other Books You May Enjoy
• Chapter 13
• Chapter 12
• Chapter 11
• Chapter 10
• Chapter 9
• Chapter 8
• Chapter 7
• Chapter 6
• Chapter 5
• Chapter 4
• Chapter 3
• Chapter 2
• Chapter 1
• Assessments
• Further reading
• Questions
• Summary
• Inverse reinforcement learning
• MAXQ Value Function Decomposition
• Hierarchical reinforcement learning
• Hindsight experience replay
• Deep Q learning from demonstrations
• Learning from human preference
• Imagination augmented agents
• Recent Advancements and Next Steps
• Further reading
• Questions
• Summary
• Car racing
• Training the network
• Replay memory
• Dueling network
• Environment wrapper functions
• Capstone Project – Car Racing Using DQN
• Further reading
• Questions
• Summary
• Proximal Policy Optimization
• Trust Region Policy Optimization
• Swinging a pendulum
• Deep deterministic policy gradient
• Lunar Lander using policy gradients
• Policy gradient
• Policy Gradients and Optimization
• Further reading
• Questions
• Summary
• Visualization in TensorBoard
• Driving up a mountain with A3C
• How A3C works
• The architecture of A3C
• The three As
• The Asynchronous Advantage Actor Critic
• The Asynchronous Advantage Actor Critic Network
• Further reading
• Questions
• Summary
• Architecture of DARQN
• DARQN
• Doom with DRQN
• Basic Doom game
• Training an agent to play Doom
• Architecture of DRQN
• DRQN
• Playing Doom with a Deep Recurrent Q Network
• Further reading
• Questions
• Summary
• Dueling network architecture
• Prioritized experience replay
• Double DQN
• Building an agent to play Atari games
• Understanding the algorithm
• Clipping rewards
• Target network
• Experience replay
• Convolutional network
• Architecture of DQN
• What is a Deep Q Network?
• Atari Games with Deep Q Network
• Further reading
• Questions
• Summary
• Classifying fashion products using CNN
• CNN architecture
• Fully connected layer
• Pooling layer
• Convolutional layer
• Convolutional neural networks
• Generating song lyrics using LSTM RNN
• Long Short-Term Memory RNN
• Backpropagation through time
• RNN
• Neural networks in TensorFlow
• Gradient descent
• Deep diving into ANN
• Activation functions
• Output layer
• Hidden layer
• Input layer
• ANNs
• Artificial neurons
• Deep Learning Fundamentals
• Further reading
• Questions
• Summary
• Contextual bandits
• Identifying the right advertisement banner using MAB
• Applications of MAB
• The Thompson sampling algorithm
• The upper confidence bound algorithm
• The softmax exploration algorithm
• The epsilon-greedy policy
• The MAB problem
• Multi-Armed Bandit Problem
• Further reading
• Questions
• Summary
• The difference between Q learning and SARSA
• Solving the taxi problem using SARSA
• SARSA
• Solving the taxi problem using Q learning
• Q learning
• TD control
• TD prediction
• TD learning
• Temporal Difference Learning
• Further reading
• Questions
• Summary
• Off-policy Monte Carlo control
• On-policy Monte Carlo control
• Monte Carlo exploration starts
• Monte Carlo control
• Let's play Blackjack with Monte Carlo
• Every visit Monte Carlo
• First visit Monte Carlo
• Monte Carlo prediction
• Estimating the value of pi using Monte Carlo
• Monte Carlo methods
• Gaming with Monte Carlo Methods
• Further reading
• Questions
• Summary
• Policy iteration
• Value iteration
• Solving the frozen lake problem
• Policy iteration
• Value iteration
• Dynamic programming
• Solving the Bellman equation
• Deriving the Bellman equation for value and Q functions
• The Bellman equation and optimality
• State-action value function (Q function)
• State value function
• The policy function
• Discount factor
• Episodic and continuous tasks
• Rewards and returns
• Markov Decision Process
• The Markov chain and Markov process
• The Markov Decision Process and Dynamic Programming
• Further reading
• Questions
• Summary
• Adding scope
• TensorBoard
• Sessions
• Computation graph
• Placeholders
• Constants
• Variables
• Variables constants and placeholders
• TensorFlow
• Building a video game bot
• OpenAI Universe
• Training a robot to walk
• Basic simulations
• OpenAI Gym
• Common error fixes
• Installing OpenAI Gym and Universe
• Installing Docker
• Installing Anaconda
• Setting up your machine
• Getting Started with OpenAI and TensorFlow
• Further reading
• Questions
• Summary
• Natural Language Processing and Computer Vision
• Finance
• Inventory management
• Manufacturing
• Medicine and healthcare
• Education
• Applications of RL
• ViZDoom
• Project Malmo
• RL-Glue
• DeepMind Lab
• OpenAI Gym and Universe
• RL platforms
• Single and multi-agent environment
• Episodic and non-episodic environment
• Continuous environment
• Discrete environment
• Partially observable environment
• Fully observable environment
• Stochastic environment
• Deterministic environment
• Types of RL environment
• Agent environment interface
• Model
• Value function
• Policy function
• Agent
• Elements of RL
• How RL differs from other ML paradigms
• RL algorithm
• What is RL?
• Introduction to Reinforcement Learning
• Reviews
• Get in touch
• Conventions used
• Download the color images
• Download the example code files
• To get the most out of this book
• What this book covers
• Who this book is for
• Preface
• Packt is searching for authors like you
• About the reviewers
• About the author
• Contributors
• PacktPub.com
• Why subscribe?
• Packt Upsell
• 版權信息
• 封面

• 封面
• 版權信息
• Packt Upsell
• Why subscribe?
• PacktPub.com
• Contributors
• About the author
• About the reviewers
• Packt is searching for authors like you
• Preface
• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Download the color images
• Conventions used
• Get in touch
• Reviews
• Introduction to Reinforcement Learning
• What is RL?
• RL algorithm
• How RL differs from other ML paradigms
• Elements of RL
• Agent
• Policy function
• Value function
• Model
• Agent environment interface
• Types of RL environment
• Deterministic environment
• Stochastic environment
• Fully observable environment
• Partially observable environment
• Discrete environment
• Continuous environment
• Episodic and non-episodic environment
• Single and multi-agent environment
• RL platforms
• OpenAI Gym and Universe
• DeepMind Lab
• RL-Glue
• Project Malmo
• ViZDoom
• Applications of RL
• Education
• Medicine and healthcare
• Manufacturing
• Inventory management
• Finance
• Natural Language Processing and Computer Vision
• Summary
• Questions
• Further reading
• Getting Started with OpenAI and TensorFlow
• Setting up your machine
• Installing Anaconda
• Installing Docker
• Installing OpenAI Gym and Universe
• Common error fixes
• OpenAI Gym
• Basic simulations
• Training a robot to walk
• OpenAI Universe
• Building a video game bot
• TensorFlow
• Variables constants and placeholders
• Variables
• Constants
• Placeholders
• Computation graph
• Sessions
• TensorBoard
• Adding scope
• Summary
• Questions
• Further reading
• The Markov Decision Process and Dynamic Programming
• The Markov chain and Markov process
• Markov Decision Process
• Rewards and returns
• Episodic and continuous tasks
• Discount factor
• The policy function
• State value function
• State-action value function (Q function)
• The Bellman equation and optimality
• Deriving the Bellman equation for value and Q functions
• Solving the Bellman equation
• Dynamic programming
• Value iteration
• Policy iteration
• Solving the frozen lake problem
• Value iteration
• Policy iteration
• Summary
• Questions
• Further reading
• Gaming with Monte Carlo Methods
• Monte Carlo methods
• Estimating the value of pi using Monte Carlo
• Monte Carlo prediction
• First visit Monte Carlo
• Every visit Monte Carlo
• Let's play Blackjack with Monte Carlo
• Monte Carlo control
• Monte Carlo exploration starts
• On-policy Monte Carlo control
• Off-policy Monte Carlo control
• Summary
• Questions
• Further reading
• Temporal Difference Learning
• TD learning
• TD prediction
• TD control
• Q learning
• Solving the taxi problem using Q learning
• SARSA
• Solving the taxi problem using SARSA
• The difference between Q learning and SARSA
• Summary
• Questions
• Further reading
• Multi-Armed Bandit Problem
• The MAB problem
• The epsilon-greedy policy
• The softmax exploration algorithm
• The upper confidence bound algorithm
• The Thompson sampling algorithm
• Applications of MAB
• Identifying the right advertisement banner using MAB
• Contextual bandits
• Summary
• Questions
• Further reading
• Deep Learning Fundamentals
• Artificial neurons
• ANNs
• Input layer
• Hidden layer
• Output layer
• Activation functions
• Deep diving into ANN
• Gradient descent
• Neural networks in TensorFlow
• RNN
• Backpropagation through time
• Long Short-Term Memory RNN
• Generating song lyrics using LSTM RNN
• Convolutional neural networks
• Convolutional layer
• Pooling layer
• Fully connected layer
• CNN architecture
• Classifying fashion products using CNN
• Summary
• Questions
• Further reading
• Atari Games with Deep Q Network
• What is a Deep Q Network?
• Architecture of DQN
• Convolutional network
• Experience replay
• Target network
• Clipping rewards
• Understanding the algorithm
• Building an agent to play Atari games
• Double DQN
• Prioritized experience replay
• Dueling network architecture
• Summary
• Questions
• Further reading
• Playing Doom with a Deep Recurrent Q Network
• DRQN
• Architecture of DRQN
• Training an agent to play Doom
• Basic Doom game
• Doom with DRQN
• DARQN
• Architecture of DARQN
• Summary
• Questions
• Further reading
• The Asynchronous Advantage Actor Critic Network
• The Asynchronous Advantage Actor Critic
• The three As
• The architecture of A3C
• How A3C works
• Driving up a mountain with A3C
• Visualization in TensorBoard
• Summary
• Questions
• Further reading
• Policy Gradients and Optimization
• Policy gradient
• Lunar Lander using policy gradients
• Deep deterministic policy gradient
• Swinging a pendulum
• Trust Region Policy Optimization
• Proximal Policy Optimization
• Summary
• Questions
• Further reading
• Capstone Project – Car Racing Using DQN
• Environment wrapper functions
• Dueling network
• Replay memory
• Training the network
• Car racing
• Summary
• Questions
• Further reading
• Recent Advancements and Next Steps
• Imagination augmented agents
• Learning from human preference
• Deep Q learning from demonstrations
• Hindsight experience replay
• Hierarchical reinforcement learning
• MAXQ Value Function Decomposition
• Inverse reinforcement learning
• Summary
• Questions
• Further reading
• Assessments
• Chapter 1
• Chapter 2
• Chapter 3
• Chapter 4
• Chapter 5
• Chapter 6
• Chapter 7
• Chapter 8
• Chapter 9
• Chapter 10
• Chapter 11
• Chapter 12
• Chapter 13
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-06-18 19:12:48