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Found 6 papers, 1 papers with code
Reward Poisoning Attack Against Offline Reinforcement Learning
To the best of our knowledge, we propose the first black-box reward poisoning attack in the general offline RL setting.
Efficient Two-Phase Offline Deep Reinforcement Learning from Preference Feedback
Our method ignores such state-actions during the second learning phase to achieve higher learning efficiency.
On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms
Efficient learning in multi-armed bandit mechanisms such as pay-per-click (PPC) auctions typically involves three challenges: 1) inducing truthful bidding behavior (incentives), 2) using personalization in the users (context), and 3) circumventing manipulations in click patterns (corruptions).
Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning
We leverage a general framework and find conditions to ensure efficient attack under a general assumption of the learning algorithms.
Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
We study reward poisoning attacks on online deep reinforcement learning (DRL), where the attacker is oblivious to the learning algorithm used by the agent and the dynamics of the environment.
Observation-Free Attacks on Stochastic Bandits
To the best of our knowledge, we develop the first data corruption attack on stochastic multi arm bandit algorithms which works without observing the algorithm's realized behavior.
