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ALPHA: Attention-based Long-horizon Pathfinding in Highly-structured Areas
Robotics · Submitted 12 Oct 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our simulation experiments demonstrate that ALPHA outperforms both globally-guided MAPF solvers and communication-learning based ones, showcasing its potential towards scalability in realistic deployments.
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The abstract, sentence by sentence
The multi-agent pathfinding (MAPF) problem seeks collision-free paths for a team of agents from their current positions to their pre-set goals in a known environment, and is an essential problem found at the core of many logistics, transportation, and general robotics applications. Existing learning-based MAPF approaches typically only let each agent make decisions based on a limited field-of-view (FOV) around its position, as a natural means to fix the input dimensions of its policy network. However, this often makes policies short-sighted, since agents lack the ability to perceive and plan for obstacles/agents beyond their FOV. To address this challenge, we propose ALPHA, a new framework combining the use of ground truth proximal (local) information and fuzzy distal (global) information to let agents sequence local decisions based on the full current state of the system, and avoid such myopicity. We further allow agents to make short-term predictions about each others' paths, as a means to reason about each others' path intentions, thereby enhancing the level of cooperation among agents at the whole system level. Our neural structure relies on a Graph Transformer architecture to allow agents to selectively combine these different sources of information and reason about their inter-dependencies at different spatial scales. Our simulation experiments demonstrate that ALPHA outperforms both globally-guided MAPF solvers and communication-learning based ones, showcasing its potential towards scalability in realistic deployments.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The multi-agent pathfinding (MAPF) problem seeks collision-free paths for a team of agents from their current positions to their pre-set goals in a known environment, and is an essential problem found at the core of many logistics, transportation, and general robotics applications. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Existing learning-based MAPF approaches typically only let each agent make decisions based on a limited field-of-view (FOV) around its position, as a natural means to fix the input dimensions of its policy network. | background 0.72 | 0.03 | 0.02 | asserted 0.99 |
| s2 | However, this often makes policies short-sighted, since agents lack the ability to perceive and plan for obstacles/agents beyond their FOV. | gap 0.81 | 0.07 | 0.02 | asserted 0.64 |
| s3 | To address this challenge, we propose ALPHA, a new framework combining the use of ground truth proximal (local) information and fuzzy distal (global) information to let agents sequence local decisions based on the full current state of the system, and avoid such myopicity. | contribution 0.88 | 0.85 | 0.11 | asserted 0.97 |
| s4 | We further allow agents to make short-term predictions about each others' paths, as a means to reason about each others' path intentions, thereby enhancing the level of cooperation among agents at the whole system level. | method 0.62 | 0.88 | 0.05 | asserted 0.82 |
| s5 | Our neural structure relies on a Graph Transformer architecture to allow agents to selectively combine these different sources of information and reason about their inter-dependencies at different spatial scales. | method 0.99 | 0.84 | 0.04 | asserted 1.00 |
| s6 | Our simulation experiments demonstrate that ALPHA outperforms both globally-guided MAPF solvers and communication-learning based ones, showcasing its potential towards scalability in realistic deployments. | result 0.96 | 0.96 | 0.84 | asserted 0.98 |
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The paper
Original abstract
The multi-agent pathfinding (MAPF) problem seeks collision-free paths for a team of agents from their current positions to their pre-set goals in a known environment, and is an essential problem found at the core of many logistics, transportation, and general robotics applications. Existing learning-based MAPF approaches typically only let each agent make decisions based on a limited field-of-view (FOV) around its position, as a natural means to fix the input dimensions of its policy network. However, this often makes policies short-sighted, since agents lack the ability to perceive and plan for obstacles/agents beyond their FOV. To address this challenge, we propose ALPHA, a new framework combining the use of ground truth proximal (local) information and fuzzy distal (global) information to let agents sequence local decisions based on the full current state of the system, and avoid such myopicity. We further allow agents to make short-term predictions about each others' paths, as a means to reason about each others' path intentions, thereby enhancing the level of cooperation among agents at the whole system level. Our neural structure relies on a Graph Transformer architecture to allow agents to selectively combine these different sources of information and reason about their inter-dependencies at different spatial scales. Our simulation experiments demonstrate that ALPHA outperforms both globally-guided MAPF solvers and communication-learning based ones, showcasing its potential towards scalability in realistic deployments.
Human review
How the literature uses it
1 arXiv paper cite this in 3 sentences
What it cites
Uses the method or tool of 2 papers
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Our encoder uses a self-attention layer [ref] after the attention focusing layer to reason about the inter-dependencies of nodes in the augmented graphs and perform context learning.
Attention, Learn to Solve Routing Problems! · Attention-focusing Layer · 0.95
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For local observations derived from precise grid states, we employ convolutional layers and pooling layers (inspired by VGGnet [ref] ) to condense the information; meanwhile, for global observations structured as graphs, we utilize encoders based on the graph transformer [ref] to integrate the information.
Very Deep Convolutional Networks for Large-Scale Image Recognition · Attention-Based Neural Network · 0.98
Compares with 1 paper
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We compare our method with five other state-of-the-art MAPF solutions, namely our previous work PRIMAL [ref] , MAPPER [ref] with global guidance, G2RL [ref] with relaxed global guidance, graph neural network-based communication method DHC [ref] , and transformer-based communication method SCRIMP [ref] .
SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding · Comparison and Analysis · 1.00
Cites as background 1 paper
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The third class of approaches uses communication learning to exchange information with each other and achieve better individual decision-making and group coordination [ref] .
SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding · RELATED WORK · 0.80
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