This book starts with an introduction to state-based reinforcement learning algorithms
involving Markov models, Bellman equations, and writing custom C# code with the aim of …

Delve into the world of reinforcement learning algorithms and apply them to different use-
cases via Python. This book covers important topics such as policy gradients and Q learning …

Deep reinforcement learning (deep RL) combines deep learning and reinforcement
learning, in which artificial agents learn to solve sequential decision-making problems. In the …

Start with the basics of reinforcement learning and explore deep learning concepts such as
deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical …

Since the recent advent of deep reinforcement learning for game play and simulated robotic
control, a multitude of new algorithms have flourished. Most are model-free algorithms which …

Humans learn best from feedback—we are encouraged to take actions that lead to positive
results while deterred by decisions with negative consequences. This reinforcement process …

Implement state-of-the-art deep reinforcement learning algorithms using Python and its
powerful libraries Key FeaturesImplement Q-learning and Markov models with Python and …

An example-rich guide for beginners to start their reinforcement and deep reinforcement
learning journey with state-of-the-art distinct algorithms Key FeaturesCovers a vast spectrum …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

The emerging field of Reinforcement Learning (RL) has led to impressive results in varied
domains like strategy games, robotics, etc. This handout aims to give a simple introduction to …