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Reinforcement learning presents an attractive paradigm to reason aboutseveral distinct aspects of sequential decision making, such as specifyingcomplex goals, planning future observations and actions, and critiquing theirutilities. However, the combined integration of these capabilities posescompeting algorithmic challenges in retaining maximal expressivity whileallowing for flexibility in modeling choices for efficient learning andinference. We present Decision Stacks, a generative framework that decomposesgoal-conditioned policy agents into 3 generative modules. These modulessimulate the temporal evolution of observations, rewards, and actions viaindependent generative models that can be learned in parallel via teacherforcing. Our framework guarantees both expressivity and flexibility indesigning individual modules to account for key factors such as architecturalbias, optimization objective and dynamics, transferrability across domains, andinference speed. Our empirical results demonstrate the effectiveness ofDecision Stacks for offline policy optimization for several MDP and POMDPenvironments, outperforming existing methods and enabling flexible generativedecision making.

Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models