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

Reinforcement learning in robotics

Robotics is associated with a high level of complexity in terms of behavior, which is difficult to hand engineer nor exhaustive enough to approach a task using supervised learning. Thus, reinforcement learning provides the kind of framework to capture such complex behavior.

Any task related to robotics is represented by high dimensional, continuous state, and action spaces. The environmental state is not fully observable. Learning in simulation alone is not enough to say the reinforcement learning agent is ready for the real world. In the case of robotics, a reinforcement learning agent should experience uncertainty in the real-world scenario but it's difficult and expensive to obtain and reproduce.

Robustness is the highest priority for robotics. In normal analytics or traditional machine learning problems, minor errors in data, pre-processing, or algorithms result in a significant change in behavior, especially for dynamic tasks. Thus, robust algorithms...