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Theoretical results on reinforcement learning with temporally abstract options

  • Doina Precup
  • Richard S. Sutton
  • Satinder Singh
Reinforcement Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

Abstract

We present new theoretical results on planning within the framework of temporally abstract reinforcement learning (Precup & Sutton, 1997; Sutton, 1995). Temporal abstraction is a key step in any decision making system that involves planning and prediction. In temporally abstract reinforcement learning, the agent is allowed to choose among “options”, whole courses of action that may be temporally extended, stochastic, and contingent on previous events. Examples of options include closed-loop policies such as picking up an object, as well as primitive actions such as joint torques. Knowledge about the consequences of options is represented by special structures called multi-time models. In this paper we focus on the theory of planning with multi-time models. We define new Bellman equations that are satisfied for sets of multi-time models. As a consequence, multi-time models can be used interchangeably with models of primitive actions in a variety of well-known planning methods including value iteration, policy improvement and policy iteration.

Keywords

Optimal Policy Goal State Markov Decision Process Policy Iteration Reward Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Doina Precup
    • 1
  • Richard S. Sutton
    • 1
  • Satinder Singh
    • 2
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherst
  2. 2.Department of Computer ScienceUniversity of Colorado Boulder

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