Improving the Learning Rate by Inducing a Transition Model

Authors:
Robert Bridle;Eric McCreath
Affiliations:
Australian National University;Australian National University
Venue:
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Year:
2004

Citing 5
Cited 1

Integrated architecture for learning, planning, and reacting based on approximating dynamic programming

Proceedings of the seventh international conference (1990) on Machine learning
Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching

Machine Learning
AgentSpeak(L): BDI agents speak out in a logical computable language

MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Logic and Learning

Logic and Learning

Declarative programming for agent applications

Autonomous Agents and Multi-Agent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In general, a reinforcement learning agent requires many trials in order to find a successful policy in a domain. In this paper we investigate inducing a transition model to reduce the number of trials required by an agent.We discuss an approach that incorporates transition model learning within a contemporary agent design.