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Hindsight Experience Replay (HER) is a multi-goal reinforcement learningalgorithm for sparse reward functions. The algorithm treats every failure as asuccess for an alternative (virtual) goal that has been achieved in theepisode. Virtual goals are randomly selected, irrespective of which are mostinstructive for the agent. In this paper, we present two improvements over theexisting HER algorithm. First, we prioritize virtual goals from which the agentwill learn more valuable information. We call this property the instructivenessof the virtual goal and define it by a heuristic measure, which expresses howwell the agent will be able to generalize from that virtual goal to actualgoals. Secondly, we reduce existing bias in HER by the removal of misleadingsamples. To test our algorithms, we built two challenging environments withsparse reward functions. Our empirical results in both environments show vastimprovement in the final success rate and sample efficiency when compared tothe original HER algorithm. A video showing experimental results is availableat https://youtu.be/3cZwfK8Nfps .

Bias-Reduced Hindsight Experience Replay with Virtual Goal Prioritization