TY - JOUR
T1 - Bias-reduced hindsight experience replay with virtual goal prioritization
AU - Manela, B.
AU - Biess, A.
N1 - Funding Information:
This research was supported in part by the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Initiative and by the Marcus Endowment Fund both at Ben-Gurion University of the Negev. This research was supported by the Israel Science Foundation (Grant No. 1627/17).
Publisher Copyright:
© 2021
PY - 2021/9/3
Y1 - 2021/9/3
N2 - 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.
AB - 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.
KW - Hindsight Experience Replay
KW - Multi-goal reinforcement learning
KW - Sparse reward function
KW - Virtual goals
UR - http://www.scopus.com/inward/record.url?scp=85105600608&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2021.02.090
DO - 10.1016/j.neucom.2021.02.090
M3 - Article
AN - SCOPUS:85105600608
SN - 0925-2312
VL - 451
SP - 305
EP - 315
JO - Neurocomputing
JF - Neurocomputing
ER -