  The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in
real life applications are of a primary concern. In particular, the robustness
of RL agents in cyber-physical systems against adversarial attacks are
especially vital since the cost of a malevolent intrusions can be extremely
high. Studies have shown Deep Neural Networks (DNN), which forms the core
decision-making unit in most modern RL algorithms, are easily subjected to
adversarial attacks. Hence, it is imperative that RL agents deployed in
real-life applications have the capability to detect and mitigate adversarial
attacks in an online fashion. An example of such a framework is the
Meta-Learned Advantage Hierarchy (MLAH) agent that utilizes a meta-learning
framework to learn policies robustly online. Since the mechanism of this
framework are still not fully explored, we conducted multiple experiments to
better understand the framework's capabilities and limitations. Our results
shows that the MLAH agent exhibits interesting coping behaviors when subjected
to different adversarial attacks to maintain a nominal reward. Additionally,
the framework exhibits a hierarchical coping capability, based on the
adaptability of the Master policy and sub-policies themselves. From empirical
results, we also observed that as the interval of adversarial attacks increase,
the MLAH agent can maintain a higher distribution of rewards, though at the
cost of higher instabilities.

Machine Learning;Cryptography and Security;Machine Learning