Adaptive Variance for Changing Sparse-Reward Environments

Xingyu Lin; Pengsheng Guo; Carlos Florensa; David Held

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arXiv · 1903.06309

Adaptive Variance for Changing Sparse-Reward Environments

Xingyu Lin·Pengsheng Guo·Carlos Florensa·David Held

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Abstract

Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration. We propose a principled way to adapt the policy for better exploration in changing sparse-reward environments. Unlike previous works which explicitly model environmental changes, we analyze the relationship between the value function and the optimal exploration for a Gaussian-parameterized policy and show that our theory leads to an effective strategy for adjusting the variance of the policy, enabling fast adapt to changes in a variety of sparse-reward environments.

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Xingyu Lin, Pengsheng Guo, Carlos Florensa, David Held. 2019-03-15. Adaptive Variance for Changing Sparse-Reward Environments. https://arxiv.org/abs/1903.06309

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