Reinforcement Learning with TensorFlow

Book Image

Reinforcement Learning with TensorFlow

By : Sayon Dutta

Book Image

Reinforcement Learning with TensorFlow

By: Sayon Dutta

Overview of this book

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and 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 gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.

Title Page

Packt Upsell

Contributors

Preface

Free Chapter

Deep Learning – Architectures and Frameworks

Deep Learning – Architectures and Frameworks

Reinforcement learning

Introduction to TensorFlow and OpenAI Gym

The pioneers and breakthroughs in reinforcement learning

Training Reinforcement Learning Agents Using OpenAI Gym

Training Reinforcement Learning Agents Using OpenAI Gym

Programming an agent using an OpenAI Gym environment

Markov Decision Process

Markov Decision Process

Markov decision processes

Partially observable Markov decision processes

Training the FrozenLake-v0 environment using MDP

Policy Gradients

Policy Gradients

The policy optimization method

Why policy optimization methods?

Policy objective functions

Temporal difference rule

Policy gradients

Agent learning pong using policy gradients

Q-Learning and Deep Q-Networks

Q-Learning and Deep Q-Networks

Why reinforcement learning?

Model based learning and model free learning

Deep Q-networks

The Monte Carlo tree search algorithm

The SARSA algorithm

Asynchronous Methods

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

Robo Everything – Real Strategy Gaming

Robo Everything – Real Strategy Gaming

Real-time strategy games

Reinforcement learning and other approaches

Reinforcement learning in RTS gaming

AlphaGo – Reinforcement Learning at Its Best

AlphaGo – Reinforcement Learning at Its Best

AlphaGo – mastering Go

Reinforcement Learning in Autonomous Driving

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

Financial Portfolio Management

Financial Portfolio Management

Problem definition

Data preparation

Reinforcement learning

Further improvements

Reinforcement Learning in Robotics

Reinforcement Learning in Robotics

Reinforcement learning in robotics

Challenges in robot reinforcement learning

Open questions and practical challenges

Deep Reinforcement Learning in Ad Tech

Deep Reinforcement Learning in Ad Tech

Computational advertising challenges and bidding strategies

Real-time bidding by reinforcement learning in display advertising

Reinforcement Learning in Image Processing

Reinforcement Learning in Image Processing

Hierarchical object detection with deep reinforcement learning

Deep Reinforcement Learning in NLP

Deep Reinforcement Learning in NLP

Text summarization

Text question answering

Further topics in Reinforcement Learning

Further topics in Reinforcement Learning

Continuous action space algorithms

Scoring mechanism in sequential models in NLP

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Programming an agent using an OpenAI Gym environment

The environment considered for this section is the Frozen Lake v0. The actual documentation of the concerned environment can be found at https://gym.openai.com/envs/FrozenLake-v0/.

This environment consists of 4 x 4 grids representing a lake. Thus, we have 16 grid blocks, where each block can be a start block(S), frozen block(F), goal block(G), or a hole block(H). Thus, the objective of the agent is to learn to navigate from start to goal without falling in the hole:

import Gym
env = Gym.make('FrozenLake-v0')    #loads the environment FrozenLake-v0
env.render()                       # will output the environment and position of the agent

-------------------
SFFF
FHFH
FFFH
HFFG

At any given state, an agent has four actions to perform, which are up, down, left, and right. The reward at each step is 0 except the one leading to the goal state, then the reward would be 1. We start from the S state and our goal is to reach the G state without...