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Existing model-based reinforcement learning methods often study perceptionmodeling and decision making separately. We introduce joint Perception andControl as Inference (PCI), a general framework to combine perception andcontrol for partially observable environments through Bayesian inference. Basedon the fact that object-level inductive biases are critical in human perceptuallearning and reasoning, we propose Object-based Perception Control (OPC), aninstantiation of PCI which manages to facilitate control using automaticdiscovered object-based representations. We develop an unsupervised end-to-endsolution and analyze the convergence of the perception model update.Experiments in a high-dimensional pixel environment demonstrate the learningeffectiveness of our object-based perception control approach. Specifically, weshow that OPC achieves good perceptual grouping quality and outperforms severalstrong baselines in accumulated rewards.

Joint Perception and Control as Inference with an Object-based Implementation