2015/07/03 by Dimitri P. Bertsekas, Bertsekas, Dimitri P. · 1 citation
Computer Science · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Reinforcement Learning in Robotics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1507.01029
openalex publication_date 2015/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we discuss ł-policy iteration, a method for exact and approximate dynamic programming. It is intermediate between the classical value iteration (VI) and policy iteration (PI) methods, and it is closely related to optimistic (also known as modified) PI, whereby each policy evaluation is done approximately, using a finite number of VI. We review the theory of the method and associated questions of bias and exploration arising in simulation-based cost function approximation. We then discuss various implementations, which offer advantages over well-established PI methods that use LSPE(ł), LSTD(ł), or TD(ł) for policy evaluation with cost function approximation. One of these implementations is based on a new simulation scheme, called geometric sampling, which uses multiple short trajectories rather than a single infinitely long trajectory.