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No DICE: An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Risto Vuorio · Jacob Beck · Greg Farquhar · Jakob Foerster · Shimon Whiteson
Event URL: https://openreview.net/forum?id=WB2dwqWZ6kU »

Meta-gradients provide a general approach for optimizing the meta-parameters of reinforcement learning (RL) algorithms. Estimation of meta-gradients is central to the performance of these meta-algorithms, and has been studied in the setting of MAML-style short-horizon meta-RL problems. In this context, prior work has investigated the estimation of the Hessian of the RL objective, as well as tackling the problem of credit assignment to pre-adaptation behavior by making a sampling correction. However, we show that Hessian estimation, implemented for example by DiCE and its variants, always add bias and can also add variance to meta-gradient estimation. DiCE-like approaches are therefore unlikely to lie on Pareto frontier of the bias-variance tradeoff and should not be pursued in the context of meta-gradients for RL. Meanwhile, the sampling correction has not been studied in the important long-horizon setting, where the inner optimization trajectories must be truncated for computational tractability. We study the bias and variance tradeoff induced by truncated backpropagation in combination with a weighted sampling correction. While prior work has implicitly chosen points in this bias-variance space, we disentangle the sources of bias and variance and present an empirical study which relates existing estimators to each other.

Author Information

Risto Vuorio (University of Oxford)

I'm a PhD student in WhiRL at University of Oxford. I'm interested in reinforcement learning and meta-learning.

Jacob Beck (Brown University)
Greg Farquhar (Deepmind)
Jakob Foerster (University of Oxford)

Jakob Foerster received a CIFAR AI chair in 2019 and is starting as an Assistant Professor at the University of Toronto and the Vector Institute in the academic year 20/21. During his PhD at the University of Oxford, he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. He has since been working as a research scientist at Facebook AI Research in California, where he will continue advancing the field up to his move to Toronto. He was the lead organizer of the first Emergent Communication (EmeCom) workshop at NeurIPS in 2017, which he has helped organize ever since.

Shimon Whiteson (University of Oxford)

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