Source-linked AI summary
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
Vahid Behzadan, Arslan Munir
TL;DR
DQNs may be vulnerable to adversarial input perturbations, raising the question of whether their learned policies can be manipulated. The paper adapts transferable adversarial-example methods to induce policies in DQNs and demonstrates vulnerability, transferability, and policy induction in a game-learning experiment.
Problem
The paper investigates whether Deep Q-Network reinforcement learning is vulnerable to adversarial perturbations and policy induction, especially under environmental interaction.
Method
The paper uses adversarial-example attacks and transferability through a target-DQN replica to craft bounded environmental or sensory perturbations for policy induction.
Results
DQNs are vulnerable to adversarial examples, adversarial examples transfer between DQNs, and policy induction is feasible with minimal environmental or sensory perturbations.
Takeaways & Limitations
The findings establish policy induction attacks as a security concern for DQNs and motivate countermeasures for deployments in critical systems.
Takeaways & Limitations
The attack model assumes the attacker can manipulate observed environmental states, while proposed protections and analytical bounds remain future work.
Abstract
from arXiv · showhide
Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.
1 Introduction
Deep Q-Networks extend reinforcement learning to complex, high-dimensional settings, but their dependence on environmental observations creates vulnerability to adversarial manipulation. This paper studies policy induction attacks that exploit this vulnerability and evaluates their feasibility on DQNs.
- Motivation: DQNs combine reinforcement learning with deep neural networks for feature selection and Q-function approximation from complex sensory inputs.Applications include learning Atari strategies, robotic manipulation, and autonomous navigation.
- Motivation: Environmental perturbations can disrupt or change learned control policies, potentially inducing unintended actions in critical systems.The paper gives intentional UAV collisions as an example.
- Contributions: The paper proposes policy induction attacks against Deep Q-Learning by adapting adversarial-example attacks against deep learning models.The attacks seek to manipulate the policy learned by a target DQN.
- Contributions: Experiments verify adversarial-example vulnerability and transferability across DQNs, and show policy induction with minimal environmental or sensory perturbations.The study evaluates the methodology on the original DQN architecture of Mnih et al.
2 Background
The paper reviews reinforcement learning as sequential decision-making under rewards, then explains how Q-learning and DQNs approximate optimal action values. It connects adversarial examples and their transferability to the vulnerability of DQN function approximators.
- Q-Learning: An MDP models reachable states, available actions, transition probabilities, and immediate rewards, with policies mapping states to actions or action probabilities.The RL objective is to maximize cumulative discounted reward.
- Q-Learning: Q-learning estimates optimal action values iteratively, while a neural-network Q-function approximates Q*(s, a) and is trained by minimizing a loss with SGD.The target uses expected reward plus discounted future value.
- Deep Q Networks: Classical Q-networks suffer from correlated observations, policy oscillations, and potentially unstable backpropagation.These issues arise from sequential data, sensitivity of policies to Q-values, and unknown reward and Q-value scales.
- Deep Q Networks: DQNs mitigate these issues with experience replay, periodically updated target parameters, and reward clipping to [−1, +1].Experience replay samples random batches from stored observations.
- Deep Q Networks: The Atari DQN processes stacks of four consecutive pixel frames and trains on randomly sampled observation tuples.Its architecture is presented as an end-to-end game-learning network.
- Adversarial Examples: Adversarial examples are slightly perturbed inputs that cause incorrect classifications and can transfer across models with different architectures or training subsets.FGSM and JSMA are among the proposed crafting algorithms, including for targeted attacks.
3 Threat Model
The threat model gives the attacker limited information about the target DQN and direct control only over the environment’s observed configuration. Perturbations must remain below a per-configuration magnitude limit.
- Attacker Knowledge: The attacker seeks to induce an arbitrary adversarial policy in a target DQN while knowing only limited target information.This may include input format, reward function, and an estimate of target-network update frequency.
- Attacker Capabilities: The attacker cannot alter the target’s architecture, parameters, reward function, or optimization mechanism.The directly manipulable parameter is the configuration of the environment observed by the target.
- Attacker Capabilities: Environment changes may be applied before observation by predicting states or after generation through faster action timing or induced delays.Examples include changing video-game pixels or rearranging objects and illuminating areas in cyber-physical settings.
- Attack Constraint: Each perturbation must have magnitude below ε, while the attacker’s perturbation domain is otherwise unrestricted.In games, this permits changing any pixel at any screen position.
4 Attack Mechanism
The attack uses a replica DQN to craft transferable adversarial states that steer the target’s learned action values toward an attacker-selected policy. It proceeds through initialization and an exploitation cycle operating alongside the target.
- Attack Rationale: DQN learning uses a native network and an auxiliary target network, whose similarity to classifier training motivates testing adversarial-example vulnerability.The auxiliary network is a periodically synchronized copy of the native network.
- Attack Rationale: Crafted next states can make the target estimate an incorrect optimal action or optimize its learned policy toward a selected action.The attacker manipulates the loss-relevant next state rather than the target network’s parameters.
- Perturbation Crafting: Because the target architecture and parameters are unknown, the attacker obtains perturbations from a replica and relies on adversarial-example transferability.The replica computes perturbation vectors that make the adversarial policy’s action optimal at the next state.
- Perturbation Crafting: FGSM and JSMA can generate the perturbation vector because the objective resembles targeted misclassification.Both methods seek perturbations that favor a designated incorrect action.
- Attack Procedure: Initialization trains an adversarial DQN and creates a randomly initialized target replica before exploitation begins.The adversarial DQN is trained using the attacker’s reward function.
- Attack Procedure: During exploitation, the attacker observes the environment, estimates the best adversarial-policy action, crafts inputs, and operates in tandem with the target.The exploitation cycle is depicted in Figure 2 and detailed in Algorithm 1.
5 Experimental Verification
Experiments evaluate DQN vulnerability to adversarial perturbations, transferability across independently trained networks, and a policy induction attack in Pong. The results show persistent attack success, transferable perturbations, and feasibility of manipulating training through pixel changes.
- Experimental setup and baseline: The attacker perturbed pixel values in the four consecutive frames representing each observed state.The threat model limits the attacker to minor environment changes while allowing observation of target interactions.
- Experimental setup and baseline: After 2 weeks, the DQN achieved winning averages above 80%.The implementation used stacked 80x80 grayscale frames and a convolutional architecture similar to the original DQN.
- Vulnerability to adversarial examples: JSMA maintained a 100% success rate, while FGSM success decreased by 1% per 100,000 observations as training progressed.Both methods were evaluated on 100 random observations to induce a non-optimal game action.
- Transferability: More than 70% of perturbations generated by both FGSM and JSMA also affected a separately trained Q-network.The second network had a similar architecture and was trained on the same experience memory, but randomization and SGD produced different weights.
- Policy induction attack: The policy induction experiment used an adversarial DQN with rewards opposite to the target objective and JSMA-crafted observations to steer a replica network toward adversarial actions.The target DQN continued training to maximize its original reward while the environment allowed pixel perturbations.
6 Discussion on Current Counter-Measures
The paper discusses countermeasures that exploit training-time dynamics or detect recurring perturbation patterns to mitigate policy induction attacks on DQNs.
- 6 Discussion on Current Counter-Measures: Existing adversarial-example defenses such as retraining on minimally perturbed examples aim to increase the perturbation required for attacks but have inherent shortcomings.The passage introduces two shortcomings, although the supplied text specifies only the first incompletely.
- 6 Discussion on Current Counter-Measures: Adaptive exploration–exploitation mechanisms could vary random-action probability according to the trained model’s performance.The proposed mitigation increases and decreases random-action likelihood rather than allowing it to decline monotonically.
- 6 Discussion on Current Counter-Measures: Spatio-temporal pattern recognition could detect and omit regular perturbations during learning-process preprocessing.The paper identifies this detection approach as a possible protection mechanism against recurring attack inputs.
- 6 Discussion on Current Counter-Measures: The attack mechanism assumes that decreasing random-action probability makes the target DQN increasingly likely to perform actions induced by adversarial inputs.This temporal assumption motivates the proposed adaptive exploration–exploitation mitigation.
7 Conclusions and Future Work
The paper establishes that DQN-based reinforcement learning is vulnerable to policy induction attacks and proposes an adversarial-example-based mechanism demonstrated in game learning. It frames the work as preliminary and calls for broader security studies, countermeasures, and analytical understanding.
- 7 Conclusions and Future Work: DQN-based reinforcement learning is vulnerable to policy induction attacks.The conclusion states this as a central finding of the paper.
- 7 Conclusions and Future Work: The proposed attack mechanism exploits deep neural networks’ vulnerability to adversarial examples and was demonstrated in a game-learning DQN.Experiments assessed the mechanism’s efficacy and impact in the game-learning setting.
- 7 Conclusions and Future Work: The authors call for new countermeasures against attacks on DQNs deployed in cyber-physical and critical systems.The conclusion identifies mitigation of attack effects as a needed research direction.
- 7 Conclusions and Future Work: Analytical study of network architecture and exploration mechanisms could clarify their relationships with DQN vulnerability to policy induction.The paper presents such analysis as a way to guide safer and more secure deep reinforcement-learning architectures.