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Found 3 papers, 2 papers with code
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.
Multi-Agent Path Finding
Multi-agent Reinforcement Learning
+1
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
With a simple training scheme and implementation, HiMAP demonstrates competitive results in terms of success rate and scalability in the field of imitation-learning-only MAPF, showing the potential of imitation-learning-only MAPF equipped with inference techniques.
Explaining How a Neural Network Play the Go Game and Let People Learn
The AI model has surpassed human players in the game of Go, and it is widely believed that the AI model has encoded new knowledge about the Go game beyond human players.
