Environment Optimization for Multi-Agent Navigation

• URL: http://arxiv.org/abs/2209.11279v1
• Date: Thu, 22 Sep 2022 19:22:16 GMT
• Title: Environment Optimization for Multi-Agent Navigation
• Authors: Zhan Gao and Amanda Prorok
• Abstract summary: The goal of this paper is to consider the environment as a decision variable in a system-level optimization problem. We show, through formal proofs, under which conditions the environment can change while guaranteeing completeness. In order to accommodate a broad range of implementation scenarios, we include both online and offline optimization, and both discrete and continuous environment representations.
• Score: 11.473177123332281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the obvious influence of spatial constraints on agents' performance. Yet hand-designing improved environment layouts and structures is inefficient and potentially expensive. The goal of this paper is to consider the environment as a decision variable in a system-level optimization problem, where both agent performance and environment cost can be accounted for. We begin by proposing a novel environment optimization problem. We show, through formal proofs, under which conditions the environment can change while guaranteeing completeness (i.e., all agents reach their navigation goals). Our solution leverages a model-free reinforcement learning approach. In order to accommodate a broad range of implementation scenarios, we include both online and offline optimization, and both discrete and continuous environment representations. Numerical results corroborate our theoretical findings and validate our approach.

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