Arrow Research search

Author name cluster

Joseph Driscoll

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

JAAMAS Journal 2026 Journal Article

Confidence-Based curricula for multi-agent path finding via reinforcement learning

  • Thomy Phan
  • Joseph Driscoll
  • Sven Koenig

Abstract A wide range of real-world applications can be formulated as Multi-Agent Path Finding (MAPF) problem, where the goal is to find collision-free paths for multiple agents with individual start and goal locations. State-of-the-art MAPF solvers are mainly centralized and rely on global information, which limits their scalability and flexibility when facing changes or new maps that require expensive replanning. Multi-agent reinforcement learning (MARL) offers an alternative approach to addressing MAPF problems by learning decentralized policies that generalize across a variety of maps. While there exist some prior works that attempt to connect both areas, the proposed techniques are heavily engineered and very complex due to the integration of many mechanisms that limit generality and are expensive to use. We argue that much simpler and more general approaches are needed to enable decentralized MAPF in a sustainable manner at significantly lower cost. In this paper, we propose Confidence-based Auto-Curriculum for Team Update Stability (CACTUS) as a lightweight MARL approach to decentralized MAPF. CACTUS defines a simple reverse curriculum scheme, where the goal of each agent is randomly placed within an allocation radius around the agent’s start location. The allocation radius increases gradually as all agents improve, which is assessed by a confidence-based measure. In addition, we propose an extension called Confidence- and Conflict-Based Curriculum Learning with Allocation Radius Adaptation (C \(^3\) LARA), using weighted sampling of goal locations to improve conflict resolution in scenarios of high agent density. We provide a theoretical analysis of the strengths and limitations of CACTUS regarding exploration efficiency, optimality, and multi-agent coordination. We evaluate CACTUS and C \(^3\) LARA across various maps of different sizes, obstacle densities, and numbers of agents. Our experiments demonstrate better performance and generalization capabilities than state-of-the-art MARL approaches with less than 600, 000 trainable parameters, which is less than 5% of the neural network size of current MARL approaches to decentralized MAPF.

Details DOI

AAMAS Conference 2024 Conference Paper

Confidence-Based Curriculum Learning for Multi-Agent Path Finding

  • Thomy Phan
  • Joseph Driscoll
  • Justin Romberg
  • Sven Koenig

A wide range of real-world applications can be formulated as Multi- Agent Path Finding (MAPF) problem, where the goal is to find collision-free paths for multiple agents with individual start and goal locations. State-of-the-art MAPF solvers are mainly centralized and depend on global information, which limits their scalability and flexibility regarding changes or new maps that would require expensive replanning. Multi-agent reinforcement learning (MARL) offers an alternative way by learning decentralized policies that can generalize over a variety of maps. While there exist some prior works that attempt to connect both areas, the proposed techniques are heavily engineered and very complex due to the integration of many mechanisms that limit generality and are expensive to use. We argue that much simpler and general approaches are needed to bring the areas of MARL and MAPF closer together with significantly lower costs. In this paper, we propose Confidence-based Auto-Curriculum for Team Update Stability (CACTUS) as a lightweight MARL approach to MAPF. CACTUS defines a simple reverse curriculum scheme, where the goal of each agent is randomly placed within an allocation radius around the agent’s start location. The allocation radius increases gradually as all agents improve, which is assessed by a confidence-based measure. We evaluate CACTUS in various maps of different sizes, obstacle densities, and numbers of agents. Our experiments demonstrate better performance and generalization capabilities than state-of-the-art MARL approaches with less than 600, 000 trainable parameters, which is less than 5% of the neural network size of current MARL approaches to MAPF.

PDF
v2026.09.13