Auction-Consensus Algorithm with Learned Bidding Scheme for Multi-Robot Systems
For researchers in multi-robot systems, this work offers a scalable method to enhance decentralized task allocation by integrating learning with classical auction-consensus algorithms, though the improvement is incremental over existing CBBA.
This paper addresses the challenge of decentralized multi-robot task allocation by replacing the hand-crafted greedy bidding function in the Consensus-Based Bundle Algorithm (CBBA) with a neural bidding policy trained via reinforcement learning. The learned policies improve solution quality over classical CBBA while preserving decentralized execution, as demonstrated across varying swarm sizes.
Multi-Robot Task Allocation (MRTA) is a central challenge in decentralized multi-agent systems, where teams of robots must cooperatively assign and execute tasks under limited communication while optimizing global performance objectives. Auction-consensus algorithms, such as the Consensus-Based Bundle Algorithm (CBBA), provide scalable decentralized coordination with provable convergence, but rely on hand-crafted greedy scoring functions that often lead to suboptimal task allocations. This paper proposes a learning-enhanced auction-consensus framework in which CBBA's deterministic bidding mechanism is replaced by a neural bidding policy trained using reinforcement learning. Under a centralized training and decentralized execution paradigm, agents learn to compute task bids from partial local observations while retaining the standard auction and consensus phases for decentralized coordination. The learned bidding policy is trained using Proximal Policy Optimization with rewards shaped by proximity to globally optimal solutions obtained via mixed-integer linear programming. Multiple neural architectures are evaluated, including a Neural Additive Model, the Long Short-Term Memory (LSTM) model, and the Set Transformer Model. Experimental results across varying swarm sizes demonstrate that learned bidding policies can improve solution quality over classical CBBA while preserving decentralized execution. The proposed approach highlights the effectiveness of integrating reinforcement learning with classical distributed coordination algorithms, offering a scalable pathway toward higher-quality decentralized multi-robot task allocation.