Research map / Multi-agent systems and games / Cooperative multi-agent RL
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use MAUCE (aggregated local exploration bounds) instead of SCQL epsilon-greedy | MAUCE · 0101-Chain, 11 agents | cumulative regret | Helped |
| compare MAUCE and SCQL against random and LLR | MAUCE and SCQL · 0101-Chain | cumulative regret | Helped |
| use MAUCE vs SCQL and LLR | MAUCE · Gem Mining, 5 villages 8 mines | cumulative regret | Helped |
| use MAUCE vs LLR and SCQL | MAUCE · Wind Farm, 11 turbines | cumulative regret: 43.258 at timestep 10000, 81.373 at timestep 40000 | Helped |
| use LLR instead of random policy | LLR · Wind Farm | cumulative regret | No clear effect |
| SCQL after epsilon annealed to 0 | SCQL · 0101-Chain | cumulative regret | Mixed |
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