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Using Optimal Foraging Models to Evaluate Learned Robotic Foraging Behavior

机译：使用最佳觅食模型评估学习的机器人觅食行为

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摘要

A key challenge in designing robot teams is determining how to allocate team members to specific roles according to their abilities and the demands of the environment. In this paper we explore this issue in the context of multi-robot foraging, and we show that optimal foraging theory can be used to evaluate our work in learned multi-robot foraging tasks. We present a means by which members of a multi-robot team may use reinforcement learning to allocate themselves to specific foraging roles appropriate to their environment and their abilities. We test this approach in environments with different distributions of various types of attractors and by varying the relative effectiveness of different foraging strategies. We then examine the effectiveness of the algorithm by comparing the distributions learned by the individual robots to those predicted by several optimal foraging models. We show the resulting learned distributions are substantially similar to those predicted by the optimal foraging theory models.
机译：设计机器人团队的主要挑战是确定如何根据团队成员的能力和环境要求为其分配特定角色。在本文中，我们在多机器人觅食的背景下探索了这个问题，并且表明了最佳觅食理论可以用来评估我们在学习的多机器人觅食任务中的工作。我们提出一种方法，使多机器人团队的成员可以利用强化学习将自己分配到适合其环境和能力的特定觅食角色。我们在具有各种类型的吸引子的不同分布的环境中，并通过改变不同觅食策略的相对有效性来测试这种方法。然后，我们将各个机器人学习到的分布与几种最佳觅食模型预测的分布进行比较，从而检验算法的有效性。我们表明，所得的学习分布与最佳觅食理论模型预测的分布基本相似。

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