Environment Optimization for Multi-Agent Navigation
This addresses the problem of inefficient environment design for multi-agent navigation systems, representing an incremental improvement over traditional fixed-environment approaches.
The paper tackles the problem of inefficient hand-design of environments for multi-agent navigation by formulating environment optimization as a decision variable in a system-level optimization problem, showing through formal proofs that environment changes can guarantee completeness while using model-free reinforcement learning to achieve this.
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.