Deep Reinforcement Learning State of the art

Artificial intelligence has made big steps forward with reinforcement learning (RL) in the last century and with the advent of deep learning (DL) in the 90s especially the breakthrough of convolutional networks in computer vision field. The adoption of DL neural networks in RL in the first decade of the 21 century led to an end-to-end framework allowing a great advance in human-level agents and autonomous systems called deep reinforcement learning (DRL). In this book we will go through the development Timeline of RL and DL technologies describing the main improvements made in both fields. Then we will dive into DRL and have an overview of the state-of-the-art of this new and promising field by browsing a set of algorithms (Value optimization Policy optimization and Actor-Critic) then giving an outline of current challenges and real-world applications along with the hardware and frameworks used.