Multi-Agent Path Finding
17 papers with code • 0 benchmarks • 2 datasets
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Most implemented papers
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.
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.
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.
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
The final trained policy is applied to each agent for decentralized execution.
Multi-objective Conflict-based Search Using Safe-interval Path Planning
This paper addresses a generalization of the well known multi-agent path finding (MAPF) problem that optimizes multiple conflicting objectives simultaneously such as travel time and path risk.
Learning Selective Communication for Multi-Agent Path Finding
Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF).