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

Challenges in robot reinforcement learning

Applications of reinforcement learning in robotics include:

  • Locomotion
  • Manipulation
  • Autonomous machine control

As discussed previously, in order for a reinforcement learning agent to perform better in a real-world task it should have a well-defined, domain-specific reward function, which is hard to implement. This problem is being tackled by using techniques such as apprenticeship learning. Another approach to solve the uncertainty in reward is to continuously update the reward functions as per the state so that the most optimized policy is generated. This approach is called inverse reinforcement learning.

Robot reinforcement learning is a hard problem to solve owing to many challenges. The first being continuous state-action spaces. The decision is, as per the problem statement, whether to go for DAS algorithms or CAS algorithms. This means at what granular level the robot control should be. One big challenge is the complexity of the real-world systems...