Action Categorization for Computationally Improved Task Learning and Planning

Lakshmi Nair; Sonia Chernova

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arXiv · 1804.09856

Action Categorization for Computationally Improved Task Learning and Planning

Lakshmi Nair·Sonia Chernova

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Abstract

This paper explores the problem of task learning and planning, contributing the Action-Category Representation (ACR) to improve computational performance of both Planning and Reinforcement Learning (RL). ACR is an algorithm-agnostic, abstract data representation that maps objects to action categories (groups of actions), inspired by the psychological concept of action codes. We validate our approach in StarCraft and Lightworld domains; our results demonstrate several benefits of ACR relating to improved computational performance of planning and RL, by reducing the action space for the agent.

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Lakshmi Nair, Sonia Chernova. 2018-04-26. Action Categorization for Computationally Improved Task Learning and Planning. https://arxiv.org/abs/1804.09856

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