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

Model based learning and model free learning

In Chapter 3Markov Decision Process, we used states, actions, rewards, transition models, and discount factors to solve our Markov decision process, that is, the MDP problem. Thus, if all these elements of an MDP problem are available, we can easily use a planning algorithm to come up with a solution to the objective. This type of learning is called model based learning, where an AI agent will interact with the environment and based on its interactions, will try to approximate the environment's model, that is, the state transition model. Given the model, now the agent can try to find the optimum policy through value iteration or policy iteration.

But its not necessary for our AI agent to learn an explicit model of the environment. It can derive optimal policy directly from its interactions with the environment without building a model. This type of learning is called model free learning. Model free learning involves predicting the value function...