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arXiv · 2006.06054

Marginal Utility for Planning in Continuous or Large Discrete Action Spaces

Zaheen Farraz Ahmad·Levi H. S. Lelis·Michael Bowling
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Abstract

Sample-based planning is a powerful family of algorithms for generating intelligent behavior from a model of the environment. Generating good candidate actions is critical to the success of sample-based planners, particularly in continuous or large action spaces. Typically, candidate action generation exhausts the action space, uses domain knowledge, or more recently, involves learning a stochastic policy to provide such search guidance. In this paper we explore explicitly learning a candidate action generator by optimizing a novel objective, marginal utility. The marginal utility of an action generator measures the increase in value of an action over previously generated actions. We validate our approach in both curling, a challenging stochastic domain with continuous state and action spaces, and a location game with a discrete but large action space. We show that a generator trained with the marginal utility objective outperforms hand-coded schemes built on substantial domain knowledge, trained stochastic policies, and other natural objectives for generating actions for sampled-based planners.

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Zaheen Farraz Ahmad, Levi H. S. Lelis, Michael Bowling. 2020-06-10. Marginal Utility for Planning in Continuous or Large Discrete Action Spaces. https://arxiv.org/abs/2006.06054

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