Practical Reinforcement Learning

\"\"Cover \"\"
 \"\"Copyright\"\"
 \"\"Credits\"\"
 \"\"About the Author\"\"
 \"\"About the Reviewers\"\"
 \"\"www.PacktPub.com\"\"
 \"\"Customer Feedback\"\"
 \"\"Table of Contents\"\"
 \"\"Preface\"\"
 \"\"Chapter 1: Reinforcement Learning \"\"
 \"\"Overview of machine learning\"\"
 \"\"What is machine learning?\"\"
 \"\"Speech conversion from one language to another\"\"
 \"\"Suspicious activity detection from CCTVs\"\"
 \"\"Medical diagnostics for detecting diseases\"\"
 \"\"Supervised learning\"\"
 \"\"Unsupervised learning\"\"
 \"\"Reinforcement learning\"\"
 \"\"Introduction to reinforcement learning\"\"
 \"\"Positive reinforcement learning\"\" \"\"Negative reinforcement learning\"\"\"\"Applications of reinforcement learning\"\"
 \"\"Self-driving cars\"\"
 \"\"Drone autonomous aerial taxi\"\"
 \"\"Aerobatics autonomous helicopter\"\"
 \"\"TD-Gammon â#x80
#x93
 computer game\"\"
 \"\"AlphaGo\"\"
 \"\"The agent environment setup\"\"
 \"\"Exploration versus exploitation\"\"
 \"\"Neural network and reinforcement learning\"\"
 \"\"Reinforcement learning frameworks/toolkits\"\"
 \"\"OpenAI Gym\"\"
 \"\"Getting Started with OpenAI Gym\"\"
 \"\"Docker\"\"
 \"\"Docker installation on Windows environment\"\"
 \"\"Docker installation on a Linux environment\"\"
 \"\"Running an environment\"\" \"\"Brown-UMBC Reinforcement Learning and Planning\"\"\"\"Walkthrough with Hello GridWorld\"\"
 \"\"Hello GridWorld project\"\"
 \"\"Summary\"\"
 \"\"Chapter 2: Markov Decision Process \"\"
 \"\"Introduction to MDP\"\"
 \"\"State\"\"
 \"\"Action\"\"
 \"\"Model\"\"
 \"\"Reward\"\"
 \"\"Policy\"\"
 \"\"MDP --
more about rewards\"\"
 \"\"Optimal policy\"\"
 \"\"More about policy\"\"
 \"\"Bellman equation\"\"
 \"\"A practical example of building an MDP domain\"\"
 \"\"GridWorld\"\"
 \"\"Terminal states\"\"
 \"\"Java interfaces for MDP definitions\"\"
 \"\"Single-agent domain\"\"
 \"\"State\"\"
 \"\"Action\"\"
 \"\"Action type\"\"
 \"\"SampleModel\"\"
 \"\"Environment\"\"
 \"\"EnvironmentOutcome\"\" \"\"TransitionProb\"\"\"\"Defining a GridWorld state\"\"
 \"\"Defining a GridWorld model\"\"
 \"\"Creating the state visualizer\"\"
 \"\"Testing it out\"\"
 \"\"Markov chain\"\"
 \"\"Building an object-oriented MDP domain\"\"
 \"\"Summary\"\"
 \"\"Chapter 3: Dynamic Programming \"\"
 \"\"Learning and planning\"\"
 \"\"Evaluating a policy\"\"
 \"\"Value iteration\"\"
 \"\"Value iteration implementation using BURLAP\"\"
 \"\"Output of the value iteration\"\"
 \"\"Policy iteration\"\"
 \"\"Bellman equations\"\"
 \"\"The relationship between Bellman equations\"\"
 \"\"Summary\"\"
 \"\"Chapter 4: Temporal Difference Learning \"\"
 \"\"Introducing TD learning\"\" \"\"TD lambda\"\"\"\"Estimating from data\"\"
 \"\"Learning rate\"\"
 \"\"Properties of learning rate\"\"
 \"\"Overview of TD(1)\"\"
 \"\"An example of TD(1)\"\"
 \"\"Why TD(1) is wrong\"\"
 \"\"Overview of TD(0)\"\"
 \"\"TD lambda rule\"\"
 \"\"K-step estimator\"\"
 \"\"Relationship between k-step estimators and TD lambda\"\"
 \"\"Summary\"\"
 \"\"Chapter 5: Monte Carlo Methods \"\"
 \"\"Monte Carlo methods\"\"
 \"\"First visit Monte Carlo\"\"
 \"\"Example â#x80
#x93
 Blackjack\"\"
 \"\"Objective of the game\"\"
 \"\"Card scoring/values\"\"
 \"\"The deal\"\"
 \"\"Naturals\"\"
 \"\"The gameplay\"\"
 \"\"Applying the Monte Carlo approach\"\"
 \"\"Blackjack game implementation\"\"