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 4. Policy Gradients

So far, we have seen how to derive implicit policies from a value function with the value-based approach. Here, an agent will try to learn the policy directly. The approach is similar, any experienced agent will change the policy after witnessing it.

Value iteration, policy iteration, and Q-learning come under the value-based approach solved by dynamic programming, while the policy optimization approach involves policy gradients and union of this knowledge along with policy iteration, giving rise to actor-critic algorithms.

As per the dynamic programming method, there are a set of self-consistent equations to satisfy the Q and V values. Policy optimization is different, where policy learning happens directly, unlike deriving from the value function:

Thus, value-based methods learn the value function and we derive an implicit policy, but with policy-based methods, no value function is learned and the policy is learnt directly. The actor-critic method is more advanced...