  Model-based reinforcement learning methods typically learn models for
high-dimensional state spaces by aiming to reconstruct and predict the original
observations. However, drawing inspiration from model-free reinforcement
learning, we propose learning a latent dynamics model directly from rewards. In
this work, we introduce a model-based planning framework which learns a latent
reward prediction model and then plans in the latent state-space. The latent
representation is learned exclusively from multi-step reward prediction which
we show to be the only necessary information for successful planning. With this
framework, we are able to benefit from the concise model-free representation,
while still enjoying the data-efficiency of model-based algorithms. We
demonstrate our framework in multi-pendulum and multi-cheetah environments
where several pendulums or cheetahs are shown to the agent but only one of
which produces rewards. In these environments, it is important for the agent to
construct a concise latent representation to filter out irrelevant
observations. We find that our method can successfully learn an accurate latent
reward prediction model in the presence of the irrelevant information while
existing model-based methods fail. Planning in the learned latent state-space
shows strong performance and high sample efficiency over model-free and
model-based baselines.
