Reinforcement learning (RL) allows you to develop smart, quick, and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in artificial intelligence—from games, self-driving cars, and robots to enterprise applications that range from data center energy saving (cooling data centers) to smart warehousing solutions.
The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it's gaining so much popularity. It discusses MDPs, Monte Carlo tree searches, policy and value iteration, 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 a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
