A reinforcement learning trained neural bidding policy improves solution quality over classical CBBA in decentralized multi-robot task allocation while preserving auction-consensus coordination.
Amarl: An attention- based multiagent reinforcement learning approach to the min-max multiple traveling salesmen problem
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Auction-Consensus Algorithm with Learned Bidding Scheme for Multi-Robot Systems
A reinforcement learning trained neural bidding policy improves solution quality over classical CBBA in decentralized multi-robot task allocation while preserving auction-consensus coordination.