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While reinforcement learning (RL) methods that learn an internal model of theenvironment have the potential to be more sample efficient than theirmodel-free counterparts, learning to model raw observations from highdimensional sensors can be challenging. Prior work has addressed this challengeby learning low-dimensional representation of observations through auxiliaryobjectives, such as reconstruction or value prediction. However, the alignmentbetween these auxiliary objectives and the RL objective is often unclear. Inthis work, we propose a single objective which jointly optimizes a latent-spacemodel and policy to achieve high returns while remaining self-consistent. Thisobjective is a lower bound on expected returns. Unlike prior bounds formodel-based RL on policy exploration or model guarantees, our bound is directlyon the overall RL objective. We demonstrate that the resulting algorithmmatches or improves the sample-efficiency of the best prior model-based andmodel-free RL methods. While sample efficient methods typically arecomputationally demanding, our method attains the performance of SAC in about50% less wall-clock time.

Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective