playing-games-9

Multi-Agent Path Finding

3260 papers • 126 benchmarks • 313 datasets

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wonderren/public_cppmomapf

3 papers 21

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CPP simulated evaluation

pursuitMW

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Most implemented papers

Improving Continuous-time Conflict Based Search

K. Yakovlev, A. Andreychuk, Eli Boyarski, Roni Stern•Sat Jan 23 2021

CCBS with improvements of prioritizing conflicts, disjoint splitting, and high-level heuristics to the continuous time setting significantly outperforms vanilla CCBS, solving problems with almost twice as many agents and pushing the limits of multi-agent path finding in continuous-time domains.

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MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings

Jingyao Ren, V. Sathiyanarayanan, Eric Ewing, Baskin Senbaslar, Nora Ayanian•Tue Feb 23 2021

The deep convolutional network MAPFAST (Multi-Agent Path Finding Algorithm SelecTor) is developed, which takes a MAPF problem instance and attempts to select the fastest algorithm to use from a portfolio of algorithms.

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ITA-ECBS: A Bounded-Suboptimal Algorithm for Combined Target-Assignment and Path-Finding Problem

Yimin Tang, Sven Koenig, Jiaoyang Li•Fri May 31 2024

ITA-ECBS is introduced, the first bounded-suboptimal variant of ITA-CBS, which uses focal search to achieve efficiency and determines target assignments based on a new lower bound matrix and runs faster than ECBS-TA in 87.42% of 54,033 test cases.

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Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks

Hang Ma, Jiaoyang Li, T. K. S. Kumar, Sven Koenig•Sun May 07 2017

Two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS) are presented and it is shown that they solve all well-formed MAPD instances, a realistic subclass ofMAPD instances.

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Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

Jiaoyang Li, T. K. S. Kumar, Sven Koenig, Andrew Tinka, Scott Kiesel, Joseph W. Durham•Mon May 04 2020

This paper proposes a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of WindowedMAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it.

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EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding

Jiaoyang Li, Wheeler Ruml, Sven Koenig•Fri Oct 02 2020

This paper proposes Explicit Estimation CBS (EECBS), a new bounded-suboptimal variant of CBS that uses online learning to obtain inadmissible estimates of the cost of the solution of each high-level node and uses EES to choose which high- level node to expand next.

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Subdimensional Expansion for Multi-Objective Multi-Agent Path Finding

H. Choset, Z. Ren, S. Rathinam•Mon Feb 01 2021

This letter presents an approach that bypasses this so-called curse of dimensionality by leveraging prior multi-agent work with a framework called subdimensional expansion, and is able to find the complete Pareto-optimal set for problem instances with hundreds of solutions which the standard multi-objective A-style algorithms could not find within a bounded time.

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Distributed Heuristic Multi-Agent Path Finding with Communication

Ziyuan Ma, Yudong Luo, Hang Ma•Sat May 29 2021

This paper combines communication with deep Q-learning to provide a novel learning based method for MAPF, where agents achieve cooperation via graph convolution, to guide RL algorithm on long-horizon goal-oriented tasks.

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Multi-objective Conflict-based Search Using Safe-interval Path Planning

H. Choset, Z. Ren, S. Rathinam•Sun Aug 01 2021

This paper develops the MO-SIPP algorithm, shows its properties and embeds it in MO-CBS, and presents extensive numerical results to show that there is an order of magnitude improvement in the average low level search time and a significant improved in the success rates of finding the Pareto-optimal front.

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