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Why do reinforcement-learned swarms coordinate in simulation and struggle after deployment? This episode explains SwarmACB's proposal to learn behavior selection instead of direct wheel control. We examine three questions: how behavioral modules change the learning problem, why attention and counterfactual credit assignment fit collective control, and what the reported physical evaluation establishes. The study evaluates controllers across five collective missions using 20 e-puck robots and 250 hardware runs, with the authors reporting stronger transfer from structured policies than from continuous-action control. We connect that result to AutoMoDe's modular design philosophy and earlier work on structured representations for swarm reinforcement learning. The discussion distinguishes action abstraction, restricted observations, network capacity, and recurrent memory rather than treating them as one architectural change. Its central critical question is whether a fixed behavior library remains useful beyond missions closely aligned with its primitives. For researchers building decentralized controllers and practitioners combining learned decisions with engineered behaviors, the episode offers a concrete way to think about learning authority. Could a better action interface matter more than another round of policy scaling?
