DGBA enables reliable backdoor attacks on real-world RL policies under partial observability by learning stochastic visual triggers via conditional diffusion and using advantage-based poisoning at critical states.
Federated ensemble-directed offline reinforcement learning
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When Backdoors Meet Partial Observability: Attacking Real-World Reinforcement Learning
DGBA enables reliable backdoor attacks on real-world RL policies under partial observability by learning stochastic visual triggers via conditional diffusion and using advantage-based poisoning at critical states.