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Planning in stochastic and partially observable environments is a centralissue in artificial intelligence. One commonly used technique for solving sucha problem is by constructing an accurate model firstly. Although some recentapproaches have been proposed for learning optimal behaviour under modeluncertainty, prior knowledge about the environment is still needed to guaranteethe performance of the proposed algorithms. With the benefits of the PredictiveState Representations~(PSRs) approach for state representation and modelprediction, in this paper, we introduce an approach for planning from scratch,where an offline PSR model is firstly learned and then combined with onlineMonte-Carlo tree search for planning with model uncertainty. By comparing withthe state-of-the-art approach of planning with model uncertainty, wedemonstrated the effectiveness of the proposed approaches along with the proofof their convergence. The effectiveness and scalability of our proposedapproach are also tested on the RockSample problem, which are infeasible forthe state-of-the-art BA-POMDP based approaches.

Combining Offline Models and Online Monte-Carlo Tree Search for Planning from Scratch