INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL. (arXiv:2204.08585v1 [cs.RO])

Model-based reinforcement learning (RL) algorithms designed for handling
complex visual observations typically learn some sort of latent state
representation, either explicitly or implicitly. Standard methods of this sort
do not distinguish between functionally relevant aspects of the state and
irrelevant distractors, instead aiming to represent all available information
equally. We propose a modified objective for model-based RL that, in
combination with mutual information maximization, allows us to learn
representations and dynamics for visual model-based RL without reconstruction
in a way …

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