A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients

• Published in: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 42; no. 6; pp. 1291 - 1307
• Main Authors: Grondman, I., Busoniu, L., Lopes, G. A. D., Babuska, R.
• Format: Journal Article
• Language: English
• Published: New-York, NY IEEE 01.11.2012; Institute of Electrical and Electronics Engineers
• Subjects: Actor-critic; Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Approximation algorithms; Approximation methods; Artificial intelligence; Automatic; Automatic Control Engineering; Computer Science; Computer science; control theory; systems; Control theory. Systems; Convergence; Engineering Sciences; Equations; Estimates; Exact sciences and technology; Learning; Machine Learning; natural gradient; Optimization; Policies; policy gradient; Power control; Reinforcement; reinforcement learning (RL); Robotics; Searching; Theoretical computing; Concept learning; Action; Gradient; Finance; Reinforcement learning; Optimal policy; policy gradient; Algorithmics; Actor-critic; State space; State space method; Function approximation; Robotics; Variance; reinforcement learning (RL); Search algorithm; Gradient descent; natural gradient; Biomimetics; Power control; Learning algorithm; Artificial intelligence; actor-critic; reinforcement learning
• ISSN: 1094-6977; 1558-2442
• DOI: 10.1109/TSMCC.2012.2218595

Policy-gradient-based actor-critic algorithms are amongst the most popular algorithms in the reinforcement learning framework. Their advantage of being able to search for optimal policies using low-variance gradient estimates has made them useful in several real-life applications, such as robotics, power control, and finance. Although general surveys on reinforcement learning techniques already exist, no survey is specifically dedicated to actor-critic algorithms in particular. This paper, therefore, describes the state of the art of actor-critic algorithms, with a focus on methods that can work in an online setting and use function approximation in order to deal with continuous state and action spaces. After starting with a discussion on the concepts of reinforcement learning and the origins of actor-critic algorithms, this paper describes the workings of the natural gradient, which has made its way into many actor-critic algorithms over the past few years. A review of several standard and natural actor-critic algorithms is given, and the paper concludes with an overview of application areas and a discussion on open issues.