Bias-reduced hindsight experience replay with virtual goal prioritization

B. Manela, A. Biess

Research output: Contribution to journalArticlepeer-review

Abstract

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 https://youtu.be/xjAiwJiSeLc.

Original languageEnglish
Pages (from-to)305-315
Number of pages11
JournalNeurocomputing
Volume451
DOIs
StatePublished - 3 Sep 2021

Keywords

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

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