Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control
Deep reinforcement learning has achieved great success in many previously difficult reinforcement learning tasks, yet recent studies show that deep RL agents are also unavoidably susceptible to adversarial perturbations, similar to deep neural networks in classification tasks. Prior works mostly focus on model-free adversarial attacks and agents with discrete actions. In this work, we study the problem of continuous control agents in deep RL with adversarial attacks and propose the first two-step algorithm based on learned model dynamics. Extensive experiments on various MuJoCo domains (Cartpole, Fish, Walker, Humanoid) demonstrate that our proposed framework is much more effective and efficient than model-free based attacks baselines in degrading agent performance as well as driving agents to unsafe states.
This paper presents an adversarial attack for perturbing the actions or observations of an agent acting near-optimally in an MDP so that the policy performs poorly. I think understanding the sensitivity of a policy to slight perturbations in the actions it takes or the observations that it receives is important for having robust learned policies and controllers. This paper presents an empirical step in the direction of showing that such attacks are possible, but in the context of the other adversarial attacks that are possible, this is not surprising alone and would be much stronger with other contributions. I think an exciting direction of new work could be to continue formalizing these vulnerabilities and looking at ways of adding robustness across many other domains.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control
Deep reinforcement learning has achieved great success in many previously difficult reinforcement learning tasks, yet recent studies show that deep RL agents are also unavoidably susceptible to adversarial perturbations, similar to deep neural networks in classification tasks. Prior works mostly focus on model-free adversarial attacks and agents with discrete actions. In this work, we study the problem of continuous control agents in deep RL with adversarial attacks and propose the first two-step algorithm based on learned model dynamics. Extensive experiments on various MuJoCo domains (Cartpole, Fish, Walker, Humanoid) demonstrate that our proposed framework is much more effective and efficient than model-free based attacks baselines in degrading agent performance as well as driving agents to unsafe states.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents an adversarial attack for perturbing the actions or observations of an agent acting near-optimally in an MDP so that the policy performs poorly. I think understanding the sensitivity of a policy to slight perturbations in the actions it takes or the observations that it receives is important for having robust learned policies and controllers. This paper presents an empirical step in the direction of showing that such attacks are possible, but in the context of the other adversarial attacks that are possible, this is not surprising alone and would be much stronger with other contributions. I think an exciting direction of new work could be to continue formalizing these vulnerabilities and looking at ways of adding robustness across many other domains.
assistant
This paper presents an adversarial attack for perturbing the actions or observations of an agent acting near-optimally in an MDP so that the policy performs poorly. I think understanding the sensitivity of a policy to slight perturbations in the actions it takes or the observations that it receives is important for having robust learned policies and controllers. This paper presents an empirical step in the direction of showing that such attacks are possible, but in the context of the other adversarial attacks that are possible, this is not surprising alone and would be much stronger with other contributions. I think an exciting direction of new work could be to continue formalizing these vulnerabilities and looking at ways of adding robustness across many other domains.