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Found 11 papers, 0 papers with code
Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation
The attacker can also gradually trick the ADP learner into learning the same `nefarious' policy by consistently feeding the learner a falsified cost signal that stays close to the actual cost signal.
Reinforcement Learning for Feedback-Enabled Cyber Resilience
In this work, we review the literature on RL for cyber resilience and discuss cyber resilience against three major types of vulnerabilities, i. e., posture-related, information-related, and human-related vulnerabilities.
Game-Theoretic Frameworks for Epidemic Spreading and Human Decision Making: A Review
In this review, we motivate the game-theoretic approach to human decision-making amid epidemics.
Transactive Resilience in Renewable Microgrids: A Contract-Theoretic Approach
Renewable energy-based microgrids play a critical role in future smart grids.
Assets Defending Differential Games with Partial Information and Selected Observations
Moreover, we show that the observation choices of the defender and the attacker can be decoupled and the Nash observation strategies can be found by solving two independent optimization problems.
Self-Triggered Markov Decision Processes
We study the co-design problems of the control policy and the triggering policy to optimize two pre-specified cost criteria.
A Pursuit-Evasion Differential Game with Strategic Information Acquisition
We also show that when the game's horizon goes to infinity, the Nash observation strategy is to observe periodically, and the expected distance between the pursuer and the evader goes to zero with a bounded second moment.
Cross-Layer Coordinated Attacks on Cyber-Physical Systems: A LQG Game Framework with Controlled Observations
On the other hand, the interactions between the attacker and the defender in the physical layer significantly impact the observation and jamming strategies.
Optimal Curing Strategy for Competing Epidemics Spreading over Complex Networks
The designed curing strategy globally optimizes the trade-off between the curing cost and the severity of epidemics in the network.
Manipulating Reinforcement Learning: Poisoning Attacks on Cost Signals
Focusing on adversarial manipulation on the cost signals, we analyze the performance degradation of TD($\lambda$) and $Q$-learning algorithms under the manipulation.
Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals
This paper studies reinforcement learning (RL) under malicious falsification on cost signals and introduces a quantitative framework of attack models to understand the vulnerabilities of RL.
