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arXiv · 2207.01115

USHER: Unbiased Sampling for Hindsight Experience Replay

Liam Schramm·Yunfu Deng·Edgar Granados·Abdeslam Boularias
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Abstract

Dealing with sparse rewards is a long-standing challenge in reinforcement learning (RL). Hindsight Experience Replay (HER) addresses this problem by reusing failed trajectories for one goal as successful trajectories for another. This allows for both a minimum density of reward and for generalization across multiple goals. However, this strategy is known to result in a biased value function, as the update rule underestimates the likelihood of bad outcomes in a stochastic environment. We propose an asymptotically unbiased importance-sampling-based algorithm to address this problem without sacrificing performance on deterministic environments. We show its effectiveness on a range of robotic systems, including challenging high dimensional stochastic environments.

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Liam Schramm, Yunfu Deng, Edgar Granados, Abdeslam Boularias. 2022-07-03. USHER: Unbiased Sampling for Hindsight Experience Replay. https://arxiv.org/abs/2207.01115

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