Multi-Agent Path Finding
28 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Agent Path Finding
Libraries
Use these libraries to find Multi-Agent Path Finding models and implementations
CPP simulated evaluation
pursuitMW
Most implemented papers
Improving Continuous-time Conflict Based Search
Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps.
MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings
Solving the Multi-Agent Path Finding (MAPF) problem optimally is known to be NP-Hard for both make-span and total arrival time minimization.
Guidance Graph Optimization for Lifelong Multi-Agent Path Finding
In this work, we introduce the guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization as the task of optimizing its edge weights.
ITA-ECBS: A Bounded-Suboptimal Algorithm for the Combined Target-Assignment and Path-Finding Problem
The Combined Target-Assignment and Path-Finding (TAPF) problem, a variant of MAPF, requires one to simultaneously assign targets to agents and plan collision-free paths for agents.
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting.
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions.
EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding
ECBS is a bounded-suboptimal variant of CBS that uses focal search to speed up CBS by sacrificing optimality and instead guaranteeing that the costs of its solutions are within a given factor of optimal.
A Conflict-Based Search Framework for Multi-Objective Multi-Agent Path Finding
Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA*), to multi-agent path finding may prove to be inefficient as the dimensionality of the search space grows exponentially with the number of agents.
Subdimensional Expansion for Multi-objective Multi-agent Path Finding
One example of subdimensional expansion, when applied to A*, is called M* and M* was limited to a single objective function.
Distributed Heuristic Multi-Agent Path Finding with Communication
ZiyuanMa/DHC
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The final trained policy is applied to each agent for decentralized execution.
