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We introduce a method for policy improvement that interpolates between thegreedy approach of value-based reinforcement learning (RL) and the fullplanning approach typical of model-based RL. The new method builds on theconcept of a geometric horizon model (GHM, also known as a gamma-model), whichmodels the discounted state-visitation distribution of a given policy. We showthat we can evaluate any non-Markov policy that switches between a set of baseMarkov policies with fixed probability by a careful composition of the basepolicy GHMs, without any additional learning. We can then apply generalisedpolicy improvement (GPI) to collections of such non-Markov policies to obtain anew Markov policy that will in general outperform its precursors. We provide athorough theoretical analysis of this approach, develop applications totransfer and standard RL, and empirically demonstrate its effectiveness overstandard GPI on a challenging deep RL continuous control task. We also providean analysis of GHM training methods, proving a novel convergence resultregarding previously proposed methods and showing how to train these modelsstably in deep RL settings.

Generalised Policy Improvement with Geometric Policy Composition