This is the code repository for Reinforcement Learning with Tensorflow, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
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 datacenter 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. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as 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.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
def discretization(env, obs):
env_low = env.observation_space.low
env_high = env.observation_space.high
The following are the requirements to get the most out of this book:
- Python and TensorFlow
- Linear algebra as a prerequisite for neural networks
- Installation bundle: Python, TensorFlow, and OpenAI gym (shown in Chapter 1, Deep Learning – Architectures and Frameworks and Chapter 2, Training Reinforcement Learning Agents Using OpenAI Gym)
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