Apr 5, 2022 · We propose an RSAC (robust soft actor-critic) approach that uses a noisy state for prediction, and estimates target from nominal observation.
In this regard, we analyze whether subtle dimensionality perturbation that occurs due to the noise in the source of input at the testing time distracts agent ...
RSAC: A Robust Deep Reinforcement Learning Strategy for Dimensionality Perturbation. Surbhi Gupta, Gaurav Singal, Deepak Garg, Swagatam Das. 2022, IEEE ...
The theoretical analysis substantiates that our distillation loss guarantees to increase the prescription gap and hence improves the adversarial robustness.
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization · RSAC: A Robust Deep Reinforcement Learning Strategy for Dimensionality ...
RSAC: a robust deep reinforcement learning strategy for dimensionality perturbation. S Gupta, G Singal, D Garg, S Das. IEEE Transactions on Emerging Topics in ...
Jul 31, 2024 · We propose a novel method referred to as Transformed Input-robust RL (TIRL), which explores another avenue to mitigate the impact of adversaries.
RSAC: A Robust Deep Reinforcement Learning Strategy for Dimensionality Perturbation. Surbhi Gupta, Gaurav Singal, Deepak Garg ...
May 20, 2024 · We propose a simple but effective method, namely, Adaptive Adversarial Perturbation (A2P), which can dynamically select appropriate adversarial perturbations ...
Missing: RSAC: Dimensionality
May 2, 2024 · SAC stands for Soft Actor-Critic, a deep reinforcement learning policy. The paper proposes RSAC, a robust strategy, to handle dimensionality ...