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Do you want to play a game? Learning to play Tic-Tac-Toe in Hypermedia Environments

Katharine Beaumont, Rem Collier

TL;DR

The integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style and advisor-advisee relationships are demonstrated.

Abstract

We demonstrate the integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style. Agents use RDF knowledge stores to reason over information and apply Reinforcement Learning techniques to learn how to interact with a Tic-Tac-Toe API. Agents form advisor-advisee relationships in order to speed up individual learning and exploit and learn from data on the Web.

Do you want to play a game? Learning to play Tic-Tac-Toe in Hypermedia Environments

TL;DR

The integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style and advisor-advisee relationships are demonstrated.

Abstract

We demonstrate the integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style. Agents use RDF knowledge stores to reason over information and apply Reinforcement Learning techniques to learn how to interact with a Tic-Tac-Toe API. Agents form advisor-advisee relationships in order to speed up individual learning and exploit and learn from data on the Web.

Paper Structure

This paper contains 7 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Framework Overview
  3. Environment: Tic-Tac-Toe API
  4. Individual Agent Learning
  5. Transfer Learning
  6. Discussion

Figures (2)

  • Figure 1: System Diagram
  • Figure 2: The agent learning process