RoAd-RL is a new benchmarking library for adversarial reinforcement learning that evaluates DQN, PPO, and SAC agents across 192 attack-defense configurations and finds substantial robustness variations plus cases where defenses harm performance more than attacks.
Advancing ro- bustness in deep reinforcement learning with an ensemble defense approach
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A framework trains and compares MLP, transformer, and GAIL-based trajectory models on real driving data, finding that architectural differences cause large variations in robustness to PGD attacks despite similar nominal accuracy.
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RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
RoAd-RL is a new benchmarking library for adversarial reinforcement learning that evaluates DQN, PPO, and SAC agents across 192 attack-defense configurations and finds substantial robustness variations plus cases where defenses harm performance more than attacks.
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Real-Time Evaluation of Autonomous Systems under Adversarial Attacks
A framework trains and compares MLP, transformer, and GAIL-based trajectory models on real driving data, finding that architectural differences cause large variations in robustness to PGD attacks despite similar nominal accuracy.