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We investigate the use of temporally abstract actions, or macro-actions, inthe solution of Markov decision processes. Unlike current models that combineboth primitive actions and macro-actions and leave the state space unchanged,we propose a hierarchical model (using an abstract MDP) that works withmacro-actions only, and that significantly reduces the size of the state space.This is achieved by treating macroactions as local policies that act in certainregions of state space, and by restricting states in the abstract MDP to thoseat the boundaries of regions. The abstract MDP approximates the original andcan be solved more efficiently. We discuss several ways in which macro-actionscan be generated to ensure good solution quality. Finally, we consider ways inwhich macro-actions can be reused to solve multiple, related MDPs; and we showthat this can justify the computational overhead of macro-action generation.

Hierarchical Solution of Markov Decision Processes using Macro-actions