E XPLORING T RANSFERABILITY OF P ERTURBATIONS IN D EEP R EINFORCEMENT L EARNING
Anonymous authors
Paper under double-blind review
A BSTRACT
The use of Deep Neural Networks (DNN) as function approximators has led to striking progress for reinforcement learning algorithms and applications.
At the same time, deep reinforcement learning agents have inherited the vulnerability of DNNs to imperceptible adversarial perturbations to their inputs.
Prior work on adversarial perturbations for deep reinforcement learning has generally relied on calculating an adversarial perturbation customized to each state visited by the agent.
In this paper we propose a more realistic threat model in which the adversary computes the perturbation only once based on a single state.
Furthermore, we show that to cause a deep reinforcement learning agent to fail it is enough to have only one adversarial offset vector in a black-box setting.
We conduct experiments in various environments from the Atari baselines, and use our single-state adversaries to demonstrate the transferability of perturbations both between states of one
MDP, and between entirely different MDPs.
We believe our adversary framework reveals fundamental properties of the environments used in deep reinforcement learning training, and is a tangible step towards building robust and reliable deep reinforcement learning agents.
I NTRODUCTION
Building on the success of DNNs for image classiﬁcation, deep reinforcement learning has seen remarkable advances in various complex environments Mnih et al.
(2015); Schulman et al.
Lillicrap et al.
Along with these successes come new challenges stemming from the lack of robustness of DNNs to small adversarial perturbations to inputs.
This lack of robustness is especially critical for deep reinforcement learning, where the actions taken by the agent can have serious real-life consequences (Levin & Carrie (2018)).
The initial work on adversarial examples in deep learning by Szegedy et al.
(2014) showed that imperceptible perturbations added to images could be used to cause a DNN image classiﬁer to misclassify, and hypothesized that this was a result of the DNN’s complexity and nonlinearities.
Follow-up work by Goodfellow et al.
(2015) proposed a more computationally efﬁcient fast gradient sign method (FGSM) for computing adversarial examples, while simultaneously explaining the presence of adversarial examples as a result of DNN classiﬁers learning approximately linear functions.
The authors additionally argue that the presence of a common high-dimensional linear decision boundary is an explanation for the transferability of adversarial examples between models trained on different minibatches of the same training set or with different architecture.
The transferability of perturbations themselves, in contrast to transferability of complete adversarial examples, was investigated by Moosavi-Dezfooli et al.
The authors showed how to compute one universal perturbation from a minibatch of images that could be used to fool a classiﬁer by adding it to each image in the validation set.
The authors argue the existence of universal perturbations is explained by correlations between the local decision boundaries near many different points in the dataset.
More recently, a new body of research on adversarial perturbations for deep reinforcement learning agents has developed, demonstrating that it is possible to make a high-performing agent fail by adding perturbations to the agent’s perception of the current state Lin et al.
(2017); Sun et al.
Huang et al.
; Pattanaik et al.
; Mandlekar et al.
A major drawback of this prior work is the threat model, in which the attacker computes perturbations and adds them to the agent’s state in real-time before the state is perceived by the agent.
Such a threat model is not practically feasible considering the heavy duty optimization methods used by Lin et al.
(2017); Sun et al.
To address this we propose a new threat model where the attacker computes just one adversarial offset vector based on a single state.
Not only is this a more practical threat model from a security perspective, but the success of attacks in this model can be used to give insight into the transferability of adversarial examples and the properties of functions learned by trained deep reinforcement learning agents.
We believe the adversarial perspective is an initial step towards understanding the loss landscape and geometry of the decision boundaries of the algorithms in use, assessing the generalization capabilities of trained agents, and building robust and reliable deep reinforcement learning agents.
For these reasons in our paper we focus on the adversarial perspective and make the following contributions:
• We introduce a framework of six unique adversary types encapsulating our new threat model where the attacker is restricted to computing an adversarial perturbation based on a single state.
• We use our framework to investigate the transferability of adversarial perturbations, both between states of the same MDP and between MDPs.
• We show with experiments in the Atari baselines that single state attacks have signiﬁcant impact on agent performance.
• In particular, we show that by using only one perturbation computed based on a random state of a random episode from a completely different environment, it is possible to have a dramatic impact on the deep reinforcement learning agent’s performance.
This transferability of adversarial perturbations between environments implies that there is a correlation between the features learned by agents trained in different environments.
While these results demonstrate that deep reinforcement learning agents are indeed learning representations that generalize across environments, they also demonstrate a critical vulnerability from the security point of view that can be exploited without any knowledge of the training environment or neural architecture.
R ELATED W ORK AND B ACKGORUND 2. D EEP R EINFORCEMENT L EARNING
In this paper we examine discrete action space MDPs that are represented by a tuple: M = ( S , A , P , r, γ, s 0 ) where S is a set of continous states, A is a set of discrete actions, P : S ×
A × S → R is the transition probability, r : S ×
A → R is the reward function, γ is the discount factor, and s 0 is the initial state distribution.
The agent interacts with the environment by observing s ∈ S and taking actions a ∈ A .
The goal is to learn a policy π θ : S ×
A → R which takes an action a in state s that maximizes the cumulative discounted reward (cid:80) T − 1 t =0 γ t r ( s t , a t ) .
For an MDP M and policy π ( s, a ) we call a sequence of state, action, reward tuples, ( s i , a i , r i ) , that occurs when utilizing π ( s, a ) in M an episode.
We use p M,π to denote the probability distribution over the episodes generated by the randomness in M and the policy π .
2. A DVERSARIAL P ERTURBATION M ETHODS
In this work we create the adversarial perturbations via two different methods.
The ﬁrst is the Carlini & Wagner (2017) formulation.
In the deep reinforcement learning setup this formulation is,
[Equation]
[Equation]
where s is the unperturbed input, s adv is the adversarially perturbed input, and J ( s ) is the augmented cost function used to train the network.
The second method we use to produce the adversarial examples is the elastic net regularization (EAD) Chen et al.
(2018) adversarial formulation,
[Equation]
[Equation]
E XPLORING T RANSFERABILITY
In this work we introduce the notion of single-state transferability in deep reinforcement learning, and investigate its effects in state-of-the-art deep reinforcement learning environment baselines.
We explore single-state transferability in our proposed adversary framework, which consists of computing an adversarial perturbation from a random state, and then adding that particular adversarial perturbation to several other states, both within the same MDP and across different MDPs.
We refer to the transferability of adversarial perturbations between states in the same MDP as state transferability .
Similarly, we refer to transferability of perturbations between states in different MDPs as environment transferability .
To be able to fully explore the different types of transferability we deﬁne a framework with several adversary variants in Section 3.2.
3. T HREAT M ODEL
In our work we propose a threat model that could plausibly be deployed in a physical deep reinforcement learning setup.
Physical adversaries for image classiﬁcation have been extensively studied.
Kurakin et al.
(2017) showed that it is possible to fool a DNN by printing out an adversarial picture and then presenting the print out as an input to the classiﬁer.
Evtimov et al.
(2018) demonstrated that placing stickers on an object can also make DNN image classiﬁers fail.
Athalye et al.
(2017) showed that one can physically build an adversarial object with a 3D printer that can fool a DNN classiﬁer.
Recently, Li B. et al.
(2019) proposed a realistic physical perturbation, a camera sticker, to fool DNN image classiﬁers.
Previous work on adversary models in deep reinforcement learning has focused on computing the adversarial examples customized to each state Huang et al.
(2017); Mandlekar et al.
; Pattanaik et al.
Lin et al.
; Pinto et al.
(2017); Sun et al.
In particular, the adversary model of Lin et al.
(2017); Sun et al.
(2020) requires real-time access to the agents perception system, while using the highly computationally demanding Carlini & Wagner (2017) adversarial formulation to compute perturbations.
Such a model, where the adversarial perturbation for each input state is computed and added in real time, is ﬁrstly not computationally efﬁcient, and secondly not a realistic adversarial scenario in many settings of interest.
In our threat model we propose to only have one offset vector to add to every clean image observed by the deep reinforcement learning agent.
We believe this scenario to be more physically viable, since this single adversarial perturbation could be a change to the lens of the camera of the deep reinforcement learning agent.
3. A DVERSARIAL F RAMEWORK
In the following section we deﬁne six unique adversary models in our framework to evaluate our proposed threat model.
Given a policy π ( s, a ) we deﬁne η ( s, π ( s, a )) to be a perturbation computed based only on the state s and the policy π ( s, a ) in state s .
In all the following deﬁnitions we ﬁx a bound κ and set
[Equation]
Deﬁnition 1: Individual state adversary , A individual , is the adversary that calculates the perturbation custom to each state.
[Equation]
Deﬁnition 2: Initial state adversary , A initial , is the adversary that calculates the perturbation based on the initial state of the agent in the given episode, and adds the initial state perturbation to the rest of the states visited.
[Equation]
Deﬁnition 3: Episode independent initial state adversary , A initiale , is the adversary that calculates the perturbation based on the initial state of the agent in a random episode e , and adds the initial state perturbation to all states visited in episode e (cid:48) .
Given an episode e ∼ p M , the perturbed state is,
[Equation]
where M is the MDP and s 0 ( e ) is the initial state of episode e .
Deﬁnition 4: Episode independent random state adversary , A randome , is the adversary that calculates the perturbation based on a random state of the agent in a random episode e , and adds the random state perturbation to all the states visited in episode e (cid:48) .
Given an episode e ∼ p M , the perturbed state is,
[Equation]
where M is the MDP and s random ( e ) is a random state of episode e .
Deﬁnition 5: Environment independent initial state adversary , A initialM , is the adversary that calculates the perturbation based on the initial state of an agent in a random episode of the MDP M , and adds the initial state perturbation to all of the states visited in an episode of the MDP M (cid:48) .
Given an episode e ∼ p M , the perturbed state is,
[Equation]
where s 0 ( e, M ) is the initial state of episode e of MDP M .
Deﬁnition 6: Environment independent random state adversary , A randomM , is the adversary that calculates the perturbation based on a random state of the agent in a random episode of the MDP M , and adds the random state perturbation to all the states visited in an episode of the MDP M (cid:48) .
Given an episode e ∼ p M , the perturbed state is
[Equation]
where s random ( e, M ) is a random state of episode e of MDP M .
E XPERIMENTS
In our experiments agents are trained with DDQN Wang et al.
(2016) in the Atari environment
Bellemare et al.
(2013) from OpenAI Brockman et al.
We average over 10 episodes in our experiments.
We deﬁne the impact of the adversary on the agent by normalizing the performance drop as follows
[Equation]
Here Score max is the score of the baseline trained agent following the learned policy without the presence of an adversary, Score adv is the score of the agent under the inﬂuence of the adversary, and
Score min is the score the agent receives when choosing the action minimizing its Q ( s, a ) function in every state.
All scores are recorded at the end of an episode.
We chose this normalization because we observed that the agent can still receive non-zero reward even when choosing the worst action in each state.
In our framework the adversaries A initial , A initiale and A randome all explore different aspects of state transferability.
A randome is the most natural test for transferability between states of one MDP, while
A initial and A initiale represent more restricted adversaries that only have access to the initial state.
Similarly, A randomM is the most natural test for environment transferability, while A initialM is a more restricted version where the adversary does not have access to arbitrary states in the MDP M .
Therefore, higher impact in A randome and A randomM indicates higher state and environment transferability respectively.
Table 1 and Table 2 show impact values of six different models from our adversarial framework utilizing the Carlini & Wagner (2017) formulation and EAD formualtion respectively for various environments from the Atari baselines.
These results show a striking level of both state transferability and environment transferability.
One can observe that while the EAD formulation has signiﬁcant state transferability for all environments, the Carlini & Wagner (2017) formulation only has signiﬁcant state transferability for CrazyClimber and Pong.
Additionally, the EAD formulation also has higher environment transferability in all environments except Pong and Roadrunner.
[Table] [Table]
It is extremely surprising to notice that the impact of A individual in JamesBond is distinctily lower than A randome .
The reason for this in JamesBond is that A randome consistently shifts all actions towards action 12 while A individual causes the agent to choose different suboptimal actions in every state.
See section 4.1 for more details.
We observe that this consistent shift towards one particular action results in a much more successful attack in certain environments.
In JamesBond, there are certain obstacles that the agent must jump over in order to avoid death, and consistently taking action 12 prevents the agent from jumping far enough.
In CrazyClimber, the consistent shift towards one action results in the agent getting stuck in one state where choosing any other action would likely free it.
See our github page 1 for videos of the agent’s behavior for these cases.
[Table]
In all the experiments we set the (cid:96) 2 -norm bound κ to a level so that Gaussian noise with (cid:96) 2 -norm κ has insigniﬁcant impact.
In Table 3 we show the Gaussian noise impacts on the environments of interest with the same (cid:96) 2 -norm bound κ used in the EAD and Carlini & Wagner (2017) formulations.
It is important to note that unbounded Carlini & Wagner (2017) produces perturbations large enough in (cid:96) 2 -norm that Gaussian noise of the same magnitude has similar impact.
Therefore, we bound the perturbation to a level where Gaussian noise is not effective.
4. E MPIRICAL E VALUATION OF A CTIONS
In this subsection we will deﬁne the functions on actions taken by the agent that we will use to understand the effects of the adversarial perturbations.
In the following subsections we will use these functions to explore the decision boundaries and representations learned by the deep reinforcement learning agent.
In the OpenAI Atari baselines the discrete action set is numbered from 0 to | A | .
For this purpose for each episode we collected statistics on the probability P ( a ) of taking action a in the following scenarios:
• P base ( a ) - the fraction of states in which action a ∈ A is taken by the agent in an episode with no adversarial perturbation added.
[Figure]
• P adv ( a ) - the fraction of states in which action a ∈ A is taken by the agent in an episode with the adversarial perturbation added.
• P adv ( a, b ) - the fraction of states in which action a ∈ A would have been taken by the agent if there were no perturbation, but action b ∈ A was taken due to the added perturbation.
• P control ( a ) =
(cid:80)
b P adv ( a, b ) - the fraction of states in which action a ∈ A would have been taken by the agent if there were no perturbation, in an episode with the adversarial perturbation added.
The difference between P control ( a ) and P adv ( a ) measures how effectively the adversary changes the action taken by the agent in each state.
We refer to this as action manipulation .
As a baseline an adversary must conduct action manipulation in at least one state.
The difference between P control ( a ) and P base ( a ) measures how effectively the adversary changes the distribution of states visited by the agent.
We refer to this as state manipulation .
Unlike action manipulation it is not readily apparent that signiﬁcant state manipulation is necessary to achieve a high impact on the agent’s performance.
In Figure 1 we observe that in Roadrunner, JamesBond, and TimePilot P control ( a ) and P base ( a ) are similar.
Thus, for these environments state manipulation is relatively small.
However, signiﬁcant action manipulation is occurring.
In Pong, BankHeist, and CrazyClimber the adversary achieves both signiﬁcant state manipulation and action manipulation.
4. A CTION M ANIPULATION
In Figure 2 we show the heatmaps corresponding to P
adv ( a, b ) .
In these heatmaps we show which particular control action has been shifted to a particular adversarial action.
In some environments there is a dominant action shift towards one particular adversarial action from one control action as in CrazyClimber or in TimePilot.
In some games there are one or more clear bright columns where all the control actions are shifted towards the same particular adversarial action, for instance in JamesBond.
Recall that all experiments were conducted using a single adversarial offset vector added to every visited state.
Thus, the presence of a bright column indicates that, for many different input states, this vector points across a decision boundary that separates one particular action from
Pong
BankHeist
TimePilot
CrazyClimber
RoadRunner
JamesBond all other actions.
A plausible explanation for these bright columns is that each one corresponds to a diverse set of states for which the neighboring decision boundary geometry is highly correlated.
4. E NVIRONMENT T RANSFERABILITY
In this section we investigate transferability between environments.
In these experiments we utilize
A randomM where the perturbation is computed from a random state of a random episode of one environment, and then added to a completely different environment.
In Figure 3 we show the impacts for the A randomM adversary in six different environments.
Each row shows the environment where the perturbation is added, and each column shows the environment from which the perturbation is computed.
Note that the diagonal of the environment transferability matrix represents state transferability, while the off diagonal elements represent environment transferabilty.
One can observe intriguing properties of the Atari baselines where certain environments are highly transferable.
We show that the perturbations computed from RoadRunner are highly effective in other environments and the perturbations computed from other environments are effective in RoadRunner.
Additionally, in half of the environments higher or similar impact is achieved when transferring a perturbation from a different environment rather than using one computed in the environment itself.
This high level of environment transferability is a sign deep reinforcement learning agents are learning representations that have correlated features across different environments.
C ONCLUSION
In this paper we introduce a framework based on six different adversaries within our proposed transferability threat model.
Using our framework we investigate the transferability properties of the environments and algorithms used for deep reinforcement learning training.
We show that using a perturbation computed from a random state of a random episode of one environment can cause signiﬁcant detrimental effects in a completely different environment.
While this transferability brings several complications from the security point of view, where an attacker can manipulate an agent in a black-box setting without having access to the training environment and neural architecture, we also argue that it is a clear sign that deep reinforcement learning agents indeed learn representations that generalize across different environments.
In particular, environment transferability of perturbations indicates that agents have promising underlying generalization skills.
We believe our results can give us a perspective on the generalization capabilities of deep reinforcement learning agents, and can be instrumental in building reliable and robust deep reinforcement learning agents.
A A PPENDIX A. I NVESTIGATION OF P OLICY G RADIENT M ETHODS
In this section we provide an investigation into policy gradient algorithms.
In Table 4 we demonstrate the impact results of the adversary deﬁnitions within the proposed adversarial framework with the
EAD formulation.
Note that the A randome , A initiale , A initialM , and A randomM adversaries in this example are computed by using one single perturbation from a random state of an agent trained with DDQN and then applying this perturbation to the agent trained with A3C. It is quite striking to observe that compared to Gaussian noise the A randomM adversary has great success on degrading the agent’s performance.
Recall that the A randomM adversary is computing a single adversarial perturbation from a random state of a completely different
[Table]
A. P ERCEPTUAL S IMILARITIES
In this section we provide an analysis of the perceptual similarities between states in the same
MDP and states from different MDPs.
In particular the perceptual similarity distance P measures the difference between two inputs based on internal activations of a neural network independent from the network architecture Zhang et al.
Figure 4 demonstrates the perceptual similarity distance between states of different MDPs and states in the same MDP.
Note that the perceptual similarity between states is not correlated with environment transferability as shown in Figure 3.
In particular, the transferability from Pong to CrazyClimber is high as can be seen in Figure 3, while the perceptual similarity distance between states from the two MDPs is also high (i.e the states are perceptually dissimilar).
Thus the perturbations transfer between these two MDPs despite the perceptual dissimilarity between the states of the two different MDPs.
A.2. A RCHITECTURAL D IFFERENCES AND H YPERPARAMETERS
In this section we provide transferability results between different architectures for A randome and
A randomM adversaries.
In general we found no difference between DDQN architectures.
In the experiments in this section the perturbation is computed in the DDQN Prior Duel architecture, and then added to the agent’s observation system trained with DDQN, DDQN Prior, DDQN Duel and
DDQN Prior Duel.
Architectures
A
[Table]
A initiale
A randome
A initialM
A randomM
DDQN 0.
DDQN Prior 0.
DDQN Duel 0.
DDQN Prior-Duel 0.
DDQN hyperparameters as follows: learning rate for Adam optimizer is 5 × 10 − 5 , buffer size is 50000 , ﬁnal value of random action probability 0 .
02 , batch size 32 , discount factor is 1 . 0 .
See more details on implementation and hyperparameters in Dhariwal et al.
A. E NVIRONMENTS
In Figure 5 we show the screenshots of the MDPs considered in this paper.
We observe that some environments from the Atari baselines have extremely high transferability.
For the extreme example, the impact of the EAD formulation in Pong is 0 .
974 for A randome .
Moreover, it is 0 .
906 for A randomM
Note that this means the perturbation is computed based on a random state of a random episode from a random environment M , and still causes the agent to perform extremely poorly. TimePilot JamesBond
BankHeist RoadRunner CrazyClimber Pong