Centrala begrepp
Proposing a novel Hierarchical Cooperation Graph Learning (HCGL) model to address complex multi-agent problems with hierarchical cooperation.
Sammanfattning
The content introduces the HCGL model, focusing on ECG structure, graph operators, training, simulations, transferability, and ablation studies.
- Introduction to HCGL model for hierarchical multi-agent cooperation.
- Explanation of ECG structure with agent, cluster, and target nodes.
- Role of graph operators in manipulating ECG topology dynamically.
- Training process of graph operators with MAPPO optimizer.
- Simulations evaluating HCGL performance in Cooperative Swarm Interception benchmark.
- Testing transferability of HCGL policy in larger-scale tasks.
- Ablation studies on the number of clusters and primitive actions.
Statistik
HCGL has a success rate of 97% in CSI benchmark.
HCGL shows high zero-shot success rates in transferability simulations.
Optimal number of clusters for HCGL performance is between 12 to 14.
Citat
"The ECG model provides a unique approach to merge raw agent actions and cooperative actions into one unified action space."
"HCGL exhibits superior performance in transferability and interpretability."