DEEP REINFORCEMENT LEARNING: A SURVEY

Reinforcement learning (RL) is poised to revolutionize the sector of AI, and represents a step toward building autonomous systems with a higher-level understanding of the real world. Currently, Deep Learning (DL) is enabling reinforcement learning (RL) to scale to issues that were previously intractable, like learning to play video games directly from pixels. Deep Reinforcement Learning (DRL) algorithms are applied to AI, allowing control policies for robots to be learned directly from camera inputs within the world. The success of Reinforcement Learning (RL) is because of its strong mathematical roots within the principles of deep learning, Monte Carlo simulation, function approximation, and Artificial Intelligence (AI). Topics treated in some details during this survey are: Temporal variations, Q-Learning, semiMDPs and stochastic games. Many recent advances in Deep Reinforcement Learning (DRL), eg. Policy gradients and hierarchical Reinforcement Learning (RL), are covered besides references. Pointers to various examples of applications are provided. Since no presently available technique works in all situations, this paper tends to propose guidelines for using previous information regarding the characteristics of the control problem at hand to decide on the suitable experience replay strategy.