Student First Author: yes
Keywords: Reinforcement Learning, Multi-goal reinforcement learning, Reinforcement learning theory
TL;DR: We derive a provably unbiased variant of Hindsight Experience Replay without sacrificing HER's low variance or high sample efficiency.
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.
Supplementary Material: zip
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