Reinforcement learning for control: Performance, stability, and deep approximators

By Lucian Busoniu, Tim de Bruin, Domagoj Tolic, Jens Kober, Ivana Palunko. 2018

446 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Electrical and Electronic Engineering

Citations in the corpus

• Deep reinforcement learning for automated stock trading: an ensemble strategy (2020, unknown): “In contrast with DQN that learns indirectly through Q-values tables and suffers the curse of dimensionality problem [40], DDPG learns directly from the observations through policy gradient.”