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Title: Adversarial Policies: Attacking Deep Reinforcement Learning

Paper content:
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.
However, an attacker is not usually able to directly modify another agent's observations.
This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial?
We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents.
The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior.
We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent.
Videos are available at https://attackingrl.github.io.
The discovery of adversarial examples for image classifiers prompted a new field of research into adversarial attacks and defenses (Szegedy et al., 2014) .
Recent work has shown that deep RL policies are also vulnerable to adversarial perturbations of image observations Kos and Song, 2017) .
However, real-world RL agents inhabit natural environments populated by other agents, including humans, who can only modify observations through their actions.
We explore whether it's possible to attack a victim policy by building an adversarial policy that takes actions in a shared environment, inducing natural observations which have adversarial effects on the victim.
RL has been applied in settings as varied as autonomous driving (Dosovitskiy et al., 2017) , negotiation (Lewis et al., 2017) and automated trading (Noonan, 2017) .
In domains such as these, an attacker cannot usually directly modify the victim policy's input.
For example, in autonomous driving pedestrians and other drivers can take actions in the world that affect the camera image, but only in a physically realistic fashion.
They cannot add noise to arbitrary pixels, or make a building disappear.
Similarly, in financial trading an attacker can send orders to an exchange which will appear in the victim's market data feed, but the attacker cannot modify observations of a third party's orders.
Contributions.
Our paper makes three key contributions.
First, we have proposed a novel threat model of natural adversarial observations produced by an adversarial policy taking actions in a shared environment.
Second, we demonstrate that adversarial policies exist in a range of zero-sum simulated robotics games against state-of-the-art victims trained via self-play to be robust to adversaries.
Third, we verify the adversarial policies win by confusing the victim, not by learning a generally strong policy.
Specifically, we find the adversary induces highly off-distribution activations in the victim, and that victim performance increases when it is blind to the adversary's position.
We repeated the hyperparameter sweep for fine-tuning victim policies for the defence experiments, but obtained similar results.
For simplicity, we therefore chose to use the same hyperparameters throughout.
We used a mixture of in-house and cloud infrastructure to perform these experiments.
It takes around 8 hours to train an adversary for a single victim using 4 cores of an Intel Xeon Platinum 8000 (Skylake) processor.

assistant: text: Deep RL policies can be attacked by other agents taking actions so as to create natural observations that are adversarial.