PACT: Plan-Augmented Coordination via Trace-Graphs for Dynamic Multi-Agent Combinatorial Optimization

Key points

• To address the arising coordination challenge, we introduce PACT (Plan-Augmented Coordination via Trace-graphs).1
• PACT augments the environment graph with auxiliary nodes representing the plans of agents, which are connected to the resource nodes to indicate future dependencies.1
• The proposed message-passing scheme over the augmented graph grounds inter-agent coordination in the environment and enables proactive, rather than reactive, conflict resolution.1
• We show that PACT improves solution quality in dynamic problems relative to baselines and exhibits the best transfer to larger instances on which, remarkably, it outperforms centralized oracles with privileged access to instance uncertainty.1

Sources (1)

• [1]PACT: Plan-Augmented Coordination via Trace-Graphs for Dynamic Multi-Agent Combinatorial Optimization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:22 PM

  Multi-agent approaches have been investigated recently as a means of improving the scalability of reinforcement learning for neural combinatorial optimization.

  To address the arising coordination challenge, we introduce PACT (Plan-Augmented Coordination via Trace-graphs).

Extractive summary: sentences quoted from the sources.