  Despite its potential to improve sample complexity versus model-free
approaches, model-based reinforcement learning can fail catastrophically if the
model is inaccurate. An algorithm should ideally be able to trust an imperfect
model over a reasonably long planning horizon, and only rely on model-free
updates when the model errors get infeasibly large. In this paper, we
investigate techniques for choosing the planning horizon on a state-dependent
basis, where a state's planning horizon is determined by the maximum cumulative
model error around that state. We demonstrate that these state-dependent model
errors can be learned with Temporal Difference methods, based on a novel
approach of temporally decomposing the cumulative model errors. Experimental
results show that the proposed method can successfully adapt the planning
horizon to account for state-dependent model accuracy, significantly improving
the efficiency of policy learning compared to model-based and model-free
baselines.
