  Recent advances in Reinforcement Learning, grounded on combining classical
theoretical results with Deep Learning paradigm, led to breakthroughs in many
artificial intelligence tasks and gave birth to Deep Reinforcement Learning
(DRL) as a field of research. In this work latest DRL algorithms are reviewed
with a focus on their theoretical justification, practical limitations and
observed empirical properties.
