Book Description
Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions.
The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.
By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
What You Will Learn
- Implement state-of-the-art Reinforcement Learning algorithms from the basics
- Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more
- Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP
- Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym
- Understand how Reinforcement Learning Applications are used in robotics
Table of Contents
1: Deep Learning – Architectures and Frameworks
- Deep learning
- Reinforcement learning
- Introduction to TensorFlow and OpenAI Gym
- The pioneers and breakthroughs in reinforcement learning
- Summary
2: Training Reinforcement Learning Agents Using OpenAI Gym
- The OpenAI Gym
- Programming an agent using an OpenAI Gym environment
- Summary
3: Markov Decision Process
- Markov decision processes
- Partially observable Markov decision processes
- Training the FrozenLake-v0 environment using MDP
- Summary
4: Policy Gradients
- The policy optimization method
- Why policy optimization methods?
- Policy objective functions
- Temporal difference rule
- Policy gradients
- Agent learning pong using policy gradients
- Summary
5: Q-Learning and Deep Q-Networks
- Why reinforcement learning?
- Model based learning and model free learning
- Q-learning
- Deep Q-networks
- The Monte Carlo tree search algorithm
- The SARSA algorithm
- Summary
6: Asynchronous Methods
- Why asynchronous methods?
- Asynchronous one-step Q-learning
- Asynchronous one-step SARSA
- Asynchronous n-step Q-learning
- Asynchronous advantage actor critic
- A3C for Pong-v0 in OpenAI gym
- Summary
7: Robo Everything – Real Strategy Gaming
- Real-time strategy games
- Reinforcement learning and other approaches
- Reinforcement learning in RTS gaming
- Summary
8: AlphaGo – Reinforcement Learning at Its Best
- What is Go?
- AlphaGo – mastering Go
- AlphaGo Zero
- Summary
9: Reinforcement Learning in Autonomous Driving
- Machine learning for autonomous driving
- Reinforcement learning for autonomous driving
- Proposed frameworks for autonomous driving
- DeepTraffic – MIT simulator for autonomous driving
- Summary
10: Financial Portfolio Management
- Introduction
- Problem definition
- Data preparation
- Reinforcement learning
- Further improvements
- Summary
11: Reinforcement Learning in Robotics
- Reinforcement learning in robotics
- Challenges in robot reinforcement learning
- Open questions and practical challenges
- Key takeaways
- Summary
12: Deep Reinforcement Learning in Ad Tech
- Computational advertising challenges and bidding strategies
- Real-time bidding by reinforcement learning in display advertising
- Summary
13: Reinforcement Learning in Image Processing
- Hierarchical object detection with deep reinforcement learning
- Summary
14: Deep Reinforcement Learning in NLP
- Text summarization
- Text question answering
- Summary
