Deep reinforcement learning techniques are provided for resource allocation in a shared computing environment. An exemplary method comprises: obtaining a specification of a workflow of a plurality of concurrent workflows in a shared computing environment, wherein the specification comprises a plurality of workflow states and one or more control variables for the workflow in the shared computing environment; evaluating values of the control variables for an execution of the concurrent workflows using a reinforcement learning agent by (i) observing the states, including a current state, and (ii) obtaining an expected utility score for combinations of the control variables for the execution of the concurrent workflows given an allocation of resources of the shared computing environment corresponding to the combination of control variables in the current state; and providing an allocation of the resources of the shared computing environment reflecting the combination having the expected utility score that satisfies a predefined score criteria.