Bias-reduced hindsight experience replay with virtual goal prioritization

B. Manela, A. Biess

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm for sparse reward functions. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode. Virtual goals are randomly selected, irrespective of which are most instructive for the agent. In this paper, we present two improvements over the existing HER algorithm. First, we prioritize virtual goals from which the agent will learn more valuable information. We call this property the instructiveness of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals. Secondly, we reduce existing bias in HER by the removal of misleading samples. To test our algorithms, we built three challenging environments with sparse reward functions. Our empirical results in both environments show vast improvement in the final success rate and sample efficiency when compared to the original HER algorithm. A video showing experimental results is available at

Original languageEnglish
Pages (from-to)305-315
Number of pages11
StatePublished - 3 Sep 2021


  • Hindsight Experience Replay
  • Multi-goal reinforcement learning
  • Sparse reward function
  • Virtual goals

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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