Physically Realistic Attacks on Deep Reinforcement Learning

Deep reinforcement learning (RL) has achieved superhuman performance in
problems ranging from data center cooling to video games. RL policies
may soon be widely deployed, with research underway in autonomous driving,
negotiation and automated trading. Many potential applications are
safety-critical: automated trading failures caused Knight Capital to lose
USD 460M
, while faulty autonomous vehicles have resulted in loss of

Consequently, it is critical that RL policies are robust: both to naturally
occurring distribution shift, and to malicious attacks by adversaries.
Unfortunately, we find that RL policies which perform at a high-level in normal
situations can harbor serious vulnerabilities which can be exploited by an

Prior work has shown deep RL policies are vulnerable to small
adversarial perturbations to their observations, similar to adversarial
in image classifiers. This threat model assumes the adversary can
directly modify the victim’s sensory observation. Such low-level access is
rarely possible. For example, an autonomous vehicle’s camera image can be
influenced by other drivers, but only to a limited extent. Other drivers cannot
add noise to arbitrary pixels, or make a building disappear.

By contrast, we model the victim and adversary as agents in a shared
environment. The adversary can take a similar set of actions to the victim.
These actions may indirectly change the observations the victim sees, but only
in a physically realistic fashion.

Note that if the victim policy were to play a Nash equilibria, it would
not be exploitable by an adversary. We therefore focus on attacking victim
policies trained via self-play, a popular method that approximates Nash
equilibria. While it is known self-play may not always converge, it has
produced highly capable AI systems. For example, AlphaGo and OpenAI
Five have beaten world Go champions, and a professional Dota 2 team.

We find it is still possible to attack victim policies in this more realistic
multi-agent threat model. Specifically, we exploit state-of-the-art policies
trained by Bansal et al from OpenAI in zero-sum games between simulated
Humanoid robots. We train our adversarial policies against a fixed victim
policy, for less than 3% as many timesteps as the victim was trained for. In
other respects, it is trained similarly to the self-play opponents: we use the
same RL algorithm, Proximal Policy Optimization, and the same sparse
reward. Surprisingly, the adversarial policies reliably beat most victims,
despite not standing up and instead flailing on the ground.

In the video at the top of the post, we show victims in three different
environments playing normal self-play opponents and adversarial policies. The
Kick and Defend environment is a penalty shootout between a victim kicker and
goalie opponent. You Shall Not Pass has a victim runner trying to cross the
finish line, and an opponent blocker trying to prevent them. Sumo Humans has
two agents competing on a round arena to knock out their opponent.

In Kick and Defend and You Shall Not Pass, the adversarial policy never
stands up nor touches the victim. Instead, it positions its body in such a way
to cause the victim’s policy to take poor actions. This style of attack is
impossible in Sumo Humans, where the adversarial policy would immediately
lose if it fell over. Instead, the adversarial policy learns to kneel in the
center in a stable position, which proves surprisingly effective.

To better understand how the adversarial policies exploit their victims, we
created “masked” versions of victim policies. The masked victim always observes
a static value for the opponent position, corresponding to a typical initial
starting state. This doctored observation is then passed to the original victim

One would expect performance to degrade when the policy cannot see its
opponent, and indeed the masked victims win less often against normal
opponents. However, they are far more robust to adversarial policies. This
result shows that the adversarial policies win by taking actions to induce
natural observations that are adversarial to the victim, and not by
physically interfering with the victim.

Furthermore, these results show there is a cyclic relationship between the
policies. There is no overall strongest policy: the best policy depends on the
other player’s policy, like in rock-paper-scissors. Technically this is
known as non-transitivity: policy A beats B which beats C, yet C beats A.
This is surprising since these environments’ real-world analogs are
(approximately) transitive: professional human soccer players and sumo
wrestlers can reliably beat amateurs. Self-play assumes
and so this may be why the self-play policies are vulnerable
to attack.

Of course in general we don’t want to completely blind the victim, since this
hurts performance against normal opponents. Instead, we propose adversarial
training: fine-tuning the victim policy against the adversary that has been
trained against it. Specifically, we fine-tune for 20 million timesteps, the
same amount of experience the adversary is trained with. Half of the episodes
are against an adversary, and the other half against a normal opponent. We find
the fine-tuned victim policy is robust to the adversary it was trained against,
and suffers only a small performance drop against a normal opponent.

However, one might wonder if this fine-tuned victim is robust to our attack
, or just the adversary it was fine-tuned against. Repeating the attack
method finds a new adversarial policy:

Notably, the new adversary trips the victim up rather than just flailing
around. This suggests our new policies are meaningfully more robust (although
there may of course be failure modes we haven’t discovered).

The existence of adversarial policies has significant implications for the
training, understanding and evaluation of RL policies. First, adversarial
policies highlight the need to move beyond self-play. Promising approaches
include iteratively applying the adversarial training defence above, and
population-based training which naturally trains against a broader range
of opponents.

Second, this attack shows that RL policies can be vulnerable to adversarial
observations that are on the manifold of naturally occurring data. By contrast,
most prior work on adversarial examples has produced physically unrealistic
perturbed images.

Finally, these results highlight the limitations of current evaluation
methodologies. The victim policies have strong average-case performance against
a range of both normal opponents and random policies. Yet their worst-case
performance against adversarial policies is extremely poor. Moreover, it would
be difficult to find this worst-case by hand: the adversarial policies do not
seem like challenging opponents to human eyes. We would recommend testing
safety-critical policies by adversarial attack, constructively lower bounding
the policies’ exploitability.

To find out more, check out our paper or visit the project website
for more example videos.

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