Multi-Agent Path Finding
22 papers with code • 0 benchmarks • 2 datasets
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Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
We first propose a selective communication block to gather richer information for better agent coordination within multi-agent environments and train the model with a Q-learning-based algorithm.
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i. e., the neighborhood, of the solution, using randomized destroy heuristics and prioritized planning.
Optimizing Crowd-Aware Multi-Agent Path Finding through Local Broadcasting with Graph Neural Networks
Multi-Agent Path Finding (MAPF) in crowded environments presents a challenging problem in motion planning, aiming to find collision-free paths for all agents in the system.
Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding
To tackle this issue, we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths.
SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding
To tackle this challenge, we propose a multi-agent actor-critic method called Soft Actor-Critic with Heuristic-Based Attention (SACHA), which employs novel heuristic-based attention mechanisms for both the actors and critics to encourage cooperation among agents.
Multi-Robot Coordination and Layout Design for Automated Warehousing
We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability.
Multi-Agent Path Finding via Tree LSTM
In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL).
Toward multi-target self-organizing pursuit in a partially observable Markov game
The proposed distributed algorithm: fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit.
Leveraging Experience in Lifelong Multi-Agent Pathfinding
Therefore, a solution to one query informs the next query, which leads to similarity with respect to the agents' start and goal positions, and how collisions need to be resolved from one query to the next.
Multi-Agent Path Finding with Prioritized Communication Learning
The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy.