Improving Policy Gradient Estimates with Influence Information


Jervis Pinto, Alan Fern, Tim Bauer, Martin Erwig ;
Proceedings of the Asian Conference on Machine Learning, PMLR 20:1-18, 2011.


In reinforcement learning (RL) it is often possible to obtain sound, but incomplete, information about influences and independencies among problem variables and rewards, even when an exact domain model is unknown. For example, such information can be computed based on a partial, qualitative domain model, or via domain-specific analysis techniques. While, intuitively, such information appears useful for RL, there are no algorithms that incorporate it in a sound way. In this work, we describe how to leverage such information for improving the estimation of policy gradients, which can be used to speedup gradient-based RL. We prove general conditions under which our estimator is unbiased and show that it will typically have reduced variance compared to standard unbiased gradient estimates. We evaluate the approach in the domain of Adaptation-Based Programming where RL is used to optimize the performance of programs and independence information can be computed via standard program analysis techniques. Incorporating independence information produces a large speedup in learning on a variety of adaptive programs.

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