Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation | Paper Citation Record

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This paper cites Then, we approximate the peak of the probability by a constant multiple of Dirac’s delta function asκworstδ(˜s⋆) and distribute the remaining probability equally as 1−κworst.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Then, we approximate the peak of the probability by a constant multiple of Dirac’s delta function asκworstδ(˜s⋆) and distribute the remaining probability equally as 1−κworst

Reference 1

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This paper cites (57) 5: end for 6: else 7: Do nothing (pass) 8: end if C.3.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation (57) 5: end for 6: else 7: Do nothing (pass) 8: end if C.3

Reference 3

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 4

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 5

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This paper cites Soft Actor-Critic Algorithms and Applications.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Soft Actor-Critic Algorithms and Applications

Reference 6

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This paper cites Average episodic rewards (± standard deviation) for median- seed models of our proposed methods (V ALT-EPS, V ALT-SOFT) and other SAC baselines across four MuJoCo tasks.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Average episodic rewards (± standard deviation) for median- seed models of our proposed methods (V ALT-EPS, V ALT-SOFT) and other SAC baselines across four MuJoCo tasks

Reference 7

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 8

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This paper cites We denote the ablation setting as w/o PE, and regularization is applied in all settings, including the ablation.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation We denote the ablation setting as w/o PE, and regularization is applied in all settings, including the ablation

Reference 9

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 10

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Observation b9797397-b95e-4e70-9522-feab1d3950f9 · outbound

This paper cites We denote these settings asAdv, where indicates the adversary rate.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation We denote these settings asAdv*, where * indicates the adversary rate

Reference 11

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This paper cites B., Andrychowicz, M., Zaremba, W., and Abbeel, P.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation B., Andrychowicz, M., Zaremba, W., and Abbeel, P

Reference 13

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Observation ef5b6cc1-b2c0-4482-bfd0-f62f49c8c9ca · outbound

This paper cites Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey

Reference 14

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This paper cites L., Esfandiari, Y ., Lee, X.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation L., Esfandiari, Y ., Lee, X

Reference 16

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This paper cites Robust Reinforcement Learning using Adversarial Populations.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Robust Reinforcement Learning using Adversarial Populations

Reference 18

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 20

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 22

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This paper cites Sincef(Q,st) is a monotonically increasing function for Q, then we can say: f(Q1,st)≤f(Q2 +ϵ,st) =ϵ +f(Q2,st) =∥Q1−Q2∥st,˜at +f(Q2,st) ↔f(Q1,st)−f(Q2,st)≤∥Q1−Q2∥st,˜at.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Sincef(Q,st) is a monotonically increasing function for Q, then we can say: f(Q1,st)≤f(Q2 +ϵ,st) =ϵ +f(Q2,st) =∥Q1−Q2∥st,˜at +f(Q2,st) ↔f(Q1,st)−f(Q2,st)≤∥Q1−Q2∥st,˜at

Reference 24

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This paper cites While its effect is only slightly better in some tasks, we do not observe any disadvantages to using PER.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation While its effect is only slightly better in some tasks, we do not observe any disadvantages to using PER

Reference 29

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This paper cites However, we observe that the agent’s learning became critically slow in HalfCheetah due to delays in updating the running statistics.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation However, we observe that the agent’s learning became critically slow in HalfCheetah due to delays in updating the running statistics

Reference 30

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This paper cites EnvironmentEnv.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation EnvironmentEnv

Reference 31

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This paper cites The SAC (agent) component retains the same settings as the base SAC, while the PPO (adversary) component follows the adversary settings of ATLA-PPO (Zhang et al., 2021).

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation The SAC (agent) component retains the same settings as the base SAC, while the PPO (adversary) component follows the adversary settings of ATLA-PPO (Zhang et al., 2021)

Reference 32

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This paper cites The solid lines represent the average evaluation scores, and the shaded areas indicate standard deviations across different seeds.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation The solid lines represent the average evaluation scores, and the shaded areas indicate standard deviations across different seeds

Reference 34

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This paper cites Compared to V ALT-EPS-SAC, V ALT-SOFT-SAC introduces more hyperparameters due to adversary policy training.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Compared to V ALT-EPS-SAC, V ALT-SOFT-SAC introduces more hyperparameters due to adversary policy training

Reference 35

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This paper cites We perform multiple training runs to tune robust critic parameters (perturbation scale and regression term) around the benchmark’s attack scale.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation We perform multiple training runs to tune robust critic parameters (perturbation scale and regression term) around the benchmark’s attack scale

Reference 36

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This paper cites We denote these settings as w/o PE and w/o PI.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation We denote these settings as w/o PE and w/o PI

Reference 39

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This paper cites Adv1.0 assumes full adversarial influence (˜at∼π◦νsoft), while Adv0.0 uses the agent policy alone (at∼π).α = 4 const.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Adv1.0 assumes full adversarial influence (˜at∼π◦νsoft), while Adv0.0 uses the agent policy alone (at∼π).α = 4 const

Reference 43

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 44

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This paper cites Adversarial Attacks on Neural Network Policies.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Adversarial Attacks on Neural Network Policies

Reference 1972

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 1998

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Observation 7832744d-ee2e-4b48-b3d2-59112542e08d · outbound

This paper cites Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger

Reference 1999

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This paper cites Reinforcement Learning via Fenchel-Rockafellar Duality.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Reinforcement Learning via Fenchel-Rockafellar Duality

Reference 2005

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation OpenAI Gym

Reference 2011

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This paper cites Adversarial examples in the physical world.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Adversarial examples in the physical world

Reference 2012

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This paper cites Delving into adversarial attacks on deep policies.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Delving into adversarial attacks on deep policies

Reference 2013

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This paper cites On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

Reference 2014

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This paper cites Proximal Policy Optimization Algorithms.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Proximal Policy Optimization Algorithms

Reference 2015

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This paper cites Practical black-box attacks against 12 Off-Policy Actor-Critic for Observation Robustness: Virtual Alternative Training machine learning.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Practical black-box attacks against 12 Off-Policy Actor-Critic for Observation Robustness: Virtual Alternative Training machine learning

Reference 2016

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This paper cites f-Divergence constrained policy improvement.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation f-Divergence constrained policy improvement

Reference 2017

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This paper cites Soft Actor-Critic for Discrete Action Settings.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Soft Actor-Critic for Discrete Action Settings

Reference 2018

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Observation c144b36e-3e1f-4f92-847c-0be805b0ea9f · outbound

This paper cites Mujoco: A physics engine for model-based control.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Mujoco: A physics engine for model-based control

Reference 2019

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Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Unresolved cited work

Reference 2020

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This paper cites Furthermore, Liu et al.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation Furthermore, Liu et al

Reference 2021

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Observation 64e57ba0-bf54-4121-942e-e2ca3ae7dcdb · outbound

This paper cites The limitations of deep learning in adversarial settings.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation The limitations of deep learning in adversarial settings

Reference 2022

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This paper cites In the following, we temporarily set aside strict notation and represent expressions like ” R a∈A·,da ” as ”P a∈A·” when discussing continuous action space.

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation In the following, we temporarily set aside strict notation and represent expressions like ” R a∈A·,da ” as ”P a∈A·” when discussing continuous action space

Reference 2023

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