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

Open questions and practical challenges

As per the different challenges in reinforcement learning algorithms, they cannot be directly implemented to robotics compared to supervised learning where large scale significant progress has already been done in terms of research and better deployment.

Reinforcement learning can be introduced for various physical systems and control tasks in robotics where risk isn't very high. The reason behind this is the question of stability of a reinforcement learning model in the real-world system. All learning processes require implemented domain knowledge for better state representations and devising accurate reward functions. This requires further research and development.

Let's discuss some of the open questions for reinforcement learning algorithms that require more attention in ongoing and future research in the space of robot reinforcement learning.

Open questions

Following is a list of open, non-exhaustive questions that demand special care to deliver better...