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Learning generalizeable policies from visual input in the presence of visualdistractions is a challenging problem in reinforcement learning. Recently,there has been renewed interest in bisimulation metrics as a tool to addressthis issue; these metrics can be used to learn representations that are, inprinciple, invariant to irrelevant distractions by measuring behaviouralsimilarity between states. An accurate, unbiased, and scalable estimation ofthese metrics has proved elusive in continuous state and action scenarios. Wepropose entangled bisimulation, a bisimulation metric that allows thespecification of the distance function between states, and can be estimatedwithout bias in continuous state and action spaces. We show how entangledbisimulation can meaningfully improve over previous methods on the DistractingControl Suite (DCS), even when added on top of data augmentation techniques.

Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation