Constrained Environment Optimization for Prioritized Multi-Agent Navigation

TL;DR

This paper introduces a novel approach to multi-agent navigation by optimizing environment layouts as decision variables, using reinforcement learning to handle complex, constrained, and prioritized scenarios for improved agent performance.

Contribution

It formulates environment optimization as a stochastic problem, incorporating agent priorities and real-world constraints, and proposes a model-free RL solution with theoretical guarantees.

Findings

Environment can be optimized while ensuring agent reachability.

Reinforcement learning effectively solves the constrained optimization.

Prioritized environment optimization improves agent performance based on priorities.

Abstract

Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the influence of spatial constraints on agents' performance. Yet hand-designing conducive environment layouts 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 are incorporated. Towards this end, we propose novel problems of unprioritized and prioritized environment optimization, where the former considers agents unbiasedly and the latter accounts for agent priorities. We show, through formal proofs, under which conditions the environment can change while guaranteeing completeness (i.e., all agents reach goals), and analyze the role of agent priorities in the environment optimization. We proceed to impose real-world constraints on the environment optimization and formulate it mathematically as a constrained stochastic optimization problem. Since the relation between agents, environment and performance is challenging to model, we leverage reinforcement learning to develop a model-free solution and a primal-dual mechanism to handle constraints. Distinct information processing architectures are integrated for various implementation scenarios, including online/offline optimization and discrete/continuous environment. Numerical results corroborate the theory and demonstrate the validity and adaptability of our approach.