Multi-Agent Path Finding
25 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
Use these libraries to find Multi-Agent Path Finding models and implementations
CPP simulated evaluation
pursuitMW
Most implemented papers
Learning Selective Communication for Multi-Agent Path Finding
ziyuanma/dcc
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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).
Subdimensional Expansion Using Attention-Based Learning For Multi-Agent Path Finding
lakshayvirmani/learning-assisted-mstar
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By leveraging a Visual Transformer, we develop a learning-based single-agent planner, which plans for a single agent while paying attention to both the structure of the map and other agents with whom conflicts may happen.
Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks
The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage.
Multi-Agent Path Finding with Prioritized Communication Learning
mail-ecnu/PICO
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The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy.
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.
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.
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).
Multi-Robot Coordination and Layout Design for Automated Warehousing
lunjohnzhang/warehouse_env_gen_public
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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.
SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding
qiushi-lin/sacha
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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.
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.
