A Unified View of TD Algorithms; Introducing Full-Gradient TD and Equi-Gradient Descent TD

This paper addresses the issue of policy evaluation in Markov Decision Processes, using linear function approximation. It provides a unified view of algorithms such as TD(), LSTD(), iLSTD, residual-gradient TD. It is asserted that they all consist in minimizing a gradient function and differ by the form of this function and their means of minimizing it. Two new schemes are introduced in that framework: Full-gradient TD which uses a generalization of the principle introduced in iLSTD, and EGD TD, which reduces the gradient by successive equi-gradient descents. These three algorithms form a new intermediate family with the interesting property of making much better use of the samples than TD while keeping a gradient descent scheme, which is useful for complexity issues and optimistic policy iteration. 1 The policy evaluation problem A Markov Decision Process (MDP) describes a dynamical system and an agent. The system is described by its state s S. When considering discrete time, the age...

• Type: Journal
• Year: 2006
• Where: CORR
• Authors: Manuel Loth, Philippe Preux
• Keywords: CORR 2006, Education, Markov Decision, Policy, Policy Evaluation