Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics

Understand

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.