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Having access to a forward model enables the use of planning algorithms suchas Monte Carlo Tree Search and Rolling Horizon Evolution. Where a model isunavailable, a natural aim is to learn a model that reflects accurately thedynamics of the environment. In many situations it might not be possible andminimal glitches in the model may lead to poor performance and failure. Thispaper explores the problem of model misspecification through uncertainty-awarereinforcement learning agents. We propose a bootstrapped multi-headed neuralnetwork that learns the distribution of future states and rewards. Weexperiment with a number of schemes to extract the most likely predictions.Moreover, we also introduce a global error correction filter that applieshigh-level constraints guided by the context provided through the predictivedistribution. We illustrate our approach on Minipacman. The evaluationdemonstrates that when dealing with imperfect models, our methods exhibitincreased performance and stability, both in terms of model accuracy and in itsuse within a planning algorithm.

Bootstrapped model learning and error correction for planning with uncertainty in model-based RL