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In the present paper, we propose an extension of the Deep Planning Network(PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There hasbeen a growing demand in model predictive control (MPC) in partially observableenvironments in which complete information is unavailable because of, forexample, lack of expensive sensors. PlaNet is a promising solution to realizesuch latent MPC, as it is used to train state-space models via model-basedreinforcement learning (MBRL) and to conduct planning in the latent space.However, recent state-of-the-art strategies mentioned in MBRR literature, suchas involving uncertainty into training and planning, have not been considered,significantly suppressing the training performance. The proposed extension isto make PlaNet uncertainty-aware on the basis of Bayesian inference, in whichboth model and action uncertainty are incorporated. Uncertainty in latentmodels is represented using a neural network ensemble to approximately infermodel posteriors. The ensemble of optimal action candidates is also employed tocapture multimodal uncertainty in the optimality. The concept of the actionensemble relies on a general variational inference MPC (VI-MPC) framework andits instance, probabilistic action ensemble with trajectory sampling (PaETS).In this paper, we extend VI-MPC and PaETS, which have been originallyintroduced in previous literature, to address partially observable cases. Weexperimentally compare the performances on continuous control tasks, andconclude that our method can consistently improve the asymptotic performancecompared with PlaNet.

PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference