Successful resource allocation mechanisms maximise prosperity by encouraging recipients to make reciprocal contributions to the economy. How, then, can we design allocation mechanisms that foster sustainable exchange? Here, in an iterated multiplayer trust game, we used deep reinforcement learning (RL) to design a resource allocation mechanism that promoted sustainable contributions from human participants to a common resource pool, leading to a large surplus and an inclusive economy. The RL agent maximised human surplus relative to baseline mechanisms based on unrestricted welfare or conditional cooperation, by conditioning its generosity on available resources and temporarily sanctioning defecting players. Examining the agent policy allowed us to develop a heuristic policy that performed at similar levels. Deep reinforcement learning can be used to discover mechanisms that promote sustainable exchange.