Reinforcement learning (RL) algorithms have traditionally been thought of as trial and error learning methods that use actual control experience to incrementally improve a control policy. Sutton’s DYNA architecture demonstrated that RL algorithms can work as well using simulated experience from an environment model, and that the resulting computation was similar to doing one-step lookahead planning. Inspired by the literature on hierarchical planning, I propose learning a hierarchy of models of the environment that abstract temporal detail as a means of improving the scalability of RL algorithms. I present H-DYNA (Hierarchical DY NA), an extension to Sutton’s DYNA architecture that is able to learn such a hierarchy of abstract models. H-DYNA differs from hierarchical planners in two ways: first, the abstract models are learned using experience gained while learning to solve other tasks in the same environment, and second, the abstract models can be used to solve stochastic control tasks. Simulations on a set of compositionally-structured navigation tasks show that H-DYNA can learn to solve them faster than conventional RL algorithms. The abstract models also serve as mechanisms for achieving transfer of learning across multiple tasks.