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.
Table of Contents (21 chapters)
Title Page
Packt Upsell

Chapter 11. Reinforcement Learning in Robotics

So far, we have seen the advancements of reinforcement learning in AlphaGo, autonomous driving, portfolio management, and a lot more. Studies and research say that reinforcement learning can provide features of cognition such as animal behavior.

A close comparison with cognitive science would be the many successful implementations of reinforcement learning in dynamic robotic systems and autonomous driving. They have proved the theory behind applying reinforcement learning algorithms for real-time control of physical systems.

The use of neural networks in deep Q-networks and policy gradients removes the use of hand engineered policy and state representations. The direct implementation of CNNs in deep reinforcement learning and using image pixels as states instead of hand engineered features, became a widely accepted practice. The concept of mini batch training and separate primary and target networks brought success to deep reinforcement learning...