Temporal-difference reinforcement learning with distributed representations.

ABSTRACT: Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the believed state of the world to distribute across sets of equivalent states. Distributed exponential discounting factors produce hyperbolic discounting in the behavior of the agent itself. We examine these issues in the context of a TD RL model in which state-belief is distributed over a set of exponentially-discounting "micro-Agents", each of which has a separate discounting factor (gamma). Each microAgent maintains an independent hypothesis about the state of the world, and a separate value-estimat

SUBMITTER: Kurth-Nelson Z 

PROVIDER: S-EPMC2760757 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature