Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms

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Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the estimate of the gradient. In this paper, we formulate the variance reduction problem by describing a signal-to-noise ratio (SNR) for policy gradient algorithms, and evaluate this SNR carefully for the popular Weight Perturbation (WP) algorithm. We confirm that SNR is a good predictor of long-term learning performance, and that in our episodic formulation, the cost-to-go function is indeed the optimal baseline. We then propose two modifications to traditional model-free policy gradient algorithms in order to optimize the SNR. First, we examine WP using anisotropic sampling distributions, which introduces a bias into the update but increases the SNR; this bias can be interpreted as following the natural gradient of the cost function. Second, we show that non-Gaussian distributions can also increase the SNR, and ...
John W. Roberts, Russ Tedrake
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors John W. Roberts, Russ Tedrake
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