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Poster
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
Zaheen Ahmad · Levi Lelis · Michael Bowling

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #582
in Poster Session 3 »

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.

Keywords: Learning Theory · Optimization for Deep Networks · Deep Learning · Optimization -> Non-Convex Optimization; Theory -> Computational Complexity; Theory

Author Information

Zaheen Ahmad (University of Alberta)
Levi Lelis (Universidade Federal de Viçosa)
Michael Bowling (University of Alberta / DeepMind)

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