2020/06/10 by Zaheen Farraz Ahmad, Levi H. S. Lelis, Ahmad, Zaheen Farraz +3
Computer Science · #AI-based Problem Solving and Planning #cs.AI
paper · pdf · doi:10.48550/arxiv.2006.06054
arxiv created 2020/06/17 · arxiv updated 2020/06/18
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.