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%20addresses%20the%20challenge%20of%20expensive%20and%20high-risk%20data%20exploration%20inherent%20in%20RL%20by%20pre-training%20policies%20on%20vast%20amounts%20of%20offline%20data%2C%20enabling%20direct%20deployment%20or%20fine-tuning%20in%20real-world%20environments.%20However%2C%20this%20training%20paradigm%20can%20compromise%20policy%20robustness%2C%20leading%20to%20degraded%20performance%20in%20practical%20conditions%20due%20to%20observation%20perturbations%20or%20intentional%20attacks.%20While%20adversarial%20attacks%20and%20defenses%20have%20been%20extensively%20studied%20in%20deep%20learning%2C%20their%20application%20in%20offline%20RL%20is%20limited.%20This%20paper%20proposes%20a%20framework%20to%20enhance%20the%20robustness%20of%20offline%20RL%20models%20by%20leveraging%20advanced%20adversarial%20attacks%20and%20defenses.%20The%20framework%20attacks%20the%20actor%20and%20critic%20components%20by%20perturbing%20observations%20during%20training%20and%20using%20adversarial%20defenses%20as%20regularization%20to%20enhance%20the%20learned%20policy.%20Four%20attacks%20and%20two%20defenses%20are%20introduced%20and%20evaluated%20on%20the%20D4RL%20benchmark.%20The%20results%20show%20the%20vulnerability%20of%20both%20the%20actor%20and%20critic%20to%20attacks%20and%20the%20effectiveness%20of%20the%20defenses%20in%20improving%20policy%20robustness.%20This%20framework%20holds%20promise%20for%20enhancing%20the%20reliability%20of%20offline%20RL%20models%20in%20practical%20scenarios.)
![[정리] Learning Robust Rewards with Adversarial Inverse Reinforcement ...](https://curt-park.github.io/images/airl/cs294_lec15_policy_computation.png)
![[정리] Learning Robust Rewards with Adversarial Inverse Reinforcement ...](https://curt-park.github.io/images/airl/airl_algo1.png)
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%20methods%20against%20input%20observation%20have%20garnered%20significant%20attention%20and%20undergone%20rapid%20evolution%20due%20to%20RL's%20potential%20vulnerability.%20Although%20these%20advanced%20methods%20have%20achieved%20reasonable%20success%2C%20there%20have%20been%20two%20limitations%20when%20considering%20adversary%20in%20terms%20of%20long-term%20horizons.%20First%2C%20the%20mutual%20dependency%20between%20the%20policy%20and%20its%20corresponding%20optimal%20adversary%20limits%20the%20development%20of%20off-policy%20RL%20algorithms%3B%20although%20obtaining%20optimal%20adversary%20should%20depend%20on%20the%20current%20policy%2C%20this%20has%20restricted%20applications%20to%20off-policy%20RL.%20Second%2C%20these%20methods%20generally%20assume%20perturbations%20based%20only%20on%20the%20%24L_p%24-norm%2C%20even%20when%20prior%20knowledge%20of%20the%20perturbation%20distribution%20in%20the%20environment%20is%20available.%20We%20here%20introduce%20another%20perspective%20on%20adversarial%20RL:%20an%20f-divergence%20constrained%20problem%20with%20the%20prior%20knowledge%20distribution.%20From%20this%2C%20we%20derive%20two%20typical%20attacks%20and%20their%20corresponding%20robust%20learning%20frameworks.%20The%20evaluation%20of%20robustness%20is%20conducted%20and%20the%20results%20demonstrate%20that%20our%20proposed%20methods%20achieve%20excellent%20performance%20in%20sample-efficient%20off-policy%20RL.)
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