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Found 21 papers, 1 papers with code
Disentangling Resilience from Robustness: Contextual Dualism, Interactionism, and Game-Theoretic Paradigms
The article concludes by discussing the interplay between robustness and resilience, suggesting that a comprehensive theory of resilience and quantification metrics, and formalization through game-theoretic frameworks are necessary.
A Discrete-time Networked Competitive Bivirus SIS Model
We identify a sufficient condition for exponential convergence to the disease-free equilibrium (DFE).
Analysis, Control, and State Estimation for the Networked Competitive Multi-Virus SIR Model
Second, we propose an observation model which captures the summation of all the viruses' infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms.
Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracy
The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design.
Quantization enabled Privacy Protection in Decentralized Stochastic Optimization
In combination with the presented quantization scheme, the proposed algorithm ensures, for the first time, rigorous differential privacy in decentralized stochastic optimization without losing provable convergence accuracy.
A Networked Competitive Multi-Virus SIR Model: Analysis and Observability
This paper proposes a novel discrete-time multi-virus SIR (susceptible-infected-recovered) model that captures the spread of competing SIR epidemics over a population network.
Decentralized Multi-Task Stochastic Optimization With Compressed Communications
The paper develops algorithms and obtains performance bounds for two different models of local information availability at the nodes: (i) sample feedback, where each node has direct access to samples of the local random variable to evaluate its local cost, and (ii) bandit feedback, where samples of the random variables are not available, but only the values of the local cost functions at two random points close to the decision are available to each node.
Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration
Many real-world applications of multi-agent reinforcement learning (RL), such as multi-robot navigation and decentralized control of cyber-physical systems, involve the cooperation of agents as a team with aligned objectives.
Decentralized Q-Learning in Zero-sum Markov Games
The key challenge in this decentralized setting is the non-stationarity of the environment from an agent's perspective, since both her own payoffs and the system evolution depend on the actions of other agents, and each agent adapts her policies simultaneously and independently.
The Confluence of Networks, Games and Learning
In addition to existing research works on game-theoretic learning over networks, we highlight several new angles and research endeavors on learning in games that are related to recent developments in artificial intelligence.
Fixed-Time Nash Equilibrium Seeking in Non-Cooperative Games
We introduce a novel class of Nash equilibrium seeking dynamics for non-cooperative games with a finite number of players, where the convergence to the Nash equilibrium is bounded by a KL function with a settling time that can be upper bounded by a positive constant that is independent of the initial conditions of the players, and which can be prescribed a priori by the system designer.
Optimization and Control
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems
We find: i) the conventional RARL framework (Pinto et al., 2017) can learn a destabilizing policy if the initial policy does not enjoy the robust stability property against the adversary; and ii) with robustly stabilizing initializations, our proposed double-loop RARL algorithm provably converges to the global optimal cost while maintaining robust stability on-the-fly.
Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes
To the best of our knowledge, our work is the first to establish non-asymptotic convergence guarantees of policy-based primal-dual methods for solving infinite-horizon discounted CMDPs.
Robust Multi-Agent Reinforcement Learning with Model Uncertainty
In contrast, we model the problem as a robust Markov game, where the goal of all agents is to find policies such that no agent has the incentive to deviate, i. e., reach some equilibrium point, which is also robust to the possible uncertainty of the MARL model.
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs
We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes (MDPs).
Policy Optimization for $\mathcal{H}_{2}$ Linear Control with $\mathcal{H}_{\infty}$ Robustness Guarantee: Implicit Regularization and Global Convergence
In this paper, we study the convergence theory of PO for $\mathcal{H}_{2}$ linear control with $\mathcal{H}_{\infty}$ robustness guarantee.
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games
In this paper, we consider discrete-time partially observed mean-field games with the risk-sensitive optimality criterion.
Bayesian Persuasion with State-Dependent Quadratic Cost Measures
We also quantify the approximation error for a quantized version of a continuous distribution and show that a semi-definite program relaxation of the equivalent problem could be a benchmark lower bound for the sender's cost for large state spaces.
Computer Science and Game Theory Optimization and Control
A Multi-Agent Off-Policy Actor-Critic Algorithm for Distributed Reinforcement Learning
This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy.
Reliable Smart Road Signs
Recently, vision-based road sign classification algorithms have been shown to be vulnerable against (even) small scale adversarial interventions that are imperceptible for humans.
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information.
