Active perception in adversarial scenarios using maximum entropy deep reinforcement learning
Name
1902.05644.pdf
Description
Accepted version
Size
692.68 KB
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Unknown
Checksum (MD5)
d2ab7b3989914cf1d1ac720832ee00ba
Author(s)
Shen, Macheng
How, Jonathan P.
Date Issued
September 2019
Publisher
IEEE
Citation
Shen, Macheng and How, Jonathan P. 2019. "Active perception in adversarial scenarios using maximum entropy deep reinforcement learning."
Version
Author's final manuscript
Abstract
© 2019 IEEE. We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect further evidence to help discriminate potential threats. The main technical challenges are the partial observability of the agent intent, the adversary modeling, and the corresponding uncertainty modeling. Note that an adversary agent may act to mislead the autonomous agent by using a deceptive strategy that is learned from past experiences. We propose an approach that combines belief space planning, generative adversary modeling, and maximum entropy reinforcement learning to obtain a stochastic belief space policy. By accounting for various adversarial behaviors in the simulation framework and minimizing the predictability of the autonomous agent's action, the resulting policy is more robust to unmodeled adversarial strategies. This improved robustness is empirically shown against an adversary that adapts to and exploits the autonomous agent's policy when compared with a standard Chance-Constraint Partially Observable Markov Decision Process robust approach.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8794389
