We study drag reduction in a minimal turbulent channel flow using scientific multi-agent reinforcement learning (SMARL). The flow is controlled by blowing and suction at the wall of an open channel, with observable states derived from flow velocities sensed at adjustable heights. We explore the actions, state, and reward space of SMARL using the off-policy algorithm V-RACER. We compare single- and multi-agent setups, and compare the identified control policies against the well-known mechanism of opposition-control. Our findings demonstrate that off-policy SMARL reduces drag in various experimental setups, surpassing classical opposition-control by up to 20 percentage points.