  Deep Reinforcement Learning has shown its ability in solving complicated
problems directly from high-dimensional observations. However, in end-to-end
settings, Reinforcement Learning algorithms are not sample-efficient and
requires long training times and quantities of data. In this work, we proposed
a framework for sample-efficient Reinforcement Learning that take advantage of
state and action representations to transform a high-dimensional problem into a
low-dimensional one. Moreover, we seek to find the optimal policy mapping
latent states to latent actions. Because now the policy is learned on abstract
representations, we enforce, using auxiliary loss functions, the lifting of
such policy to the original problem domain. Results show that the novel
framework can efficiently learn low-dimensional and interpretable state and
action representations and the optimal latent policy.
