  A variety of complex mechanisms, from chemical reaction pathways to active
fluctuations, orchestrate molecular transport in intracellular environments.
Despite significant recent progress in visualizing and probing these processes,
little is known about how tunable the resulting dynamics is through external
physical controllers. Here, we demonstrate that coarse-grained, reinforcement
learning-based protocols can be developed to achieve highly localized and
targeted cargo transport by kinesin motors on intracellular tracks. These
protocols can be implemented in practice using optical tweezers, and their
feasibility is showcased within experimentally relevant parameter regimes. Our
results open new avenues for targeted control of intracellular transport
processes, especially when opportunities for control are limited.
