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

Asynchronous n-step Q-learning

The architecture of asynchronous n-step Q-learning is, to an extent, similar to that of asynchronous one-step Q-learning. The difference is that the learning agent actions are selected using the exploration policy for up to

 steps or until a terminal state is reached, in order to compute a single update of policy network parameters. This process lists 

 rewards from the environment since its last update. Then, for each time step, the loss is calculated as the difference between the discounted future rewards at that time step and the estimated Q-value. The gradient of this loss with respect to thread-specific network parameters for each time step is calculated and accumulated. There are multiple such learning agents running and accumulating the gradients in parallel. These accumulated gradients are used to perform asynchronous updates of policy network parameters.

The pseudo-code for asynchronous n-step Q-learning is shown below. Here, the following are the global...