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

Markov decision processes

As already mentioned, an MDP is a reinforcement learning approach in a gridworld environment containing sets of states, actions, and rewards, following the Markov property to obtain an optimal policy. MDP is defined as the collection of the following:

  • States: S
  • Actions: A(s), A
  • Transition model: T(s,a,s') ~ P(s'|s,a)
  • Rewards: R(s), R(s,a), R(s,a,s')
  • Policy:
     is the optimal policy

In the case of an MDP, the environment is fully observable, that is, whatever observation the agent makes at any point in time is enough to make an optimal decision. In case of a partially observable environment, the agent needs a memory to store the past observations to make the best possible decisions.

Let's try to break this into different lego blocks to understand what this overall process means. 

The Markov property

In short, as per the Markov property, in order to know the information of near future (say, at time t+1) the present information at time t matters. 

Given a sequence, 

, the first...