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Found 12 papers, 0 papers with code
Learning How to Strategically Disclose Information
In this work, we consider an online version of information design where a sender interacts with a receiver of an unknown type who is adversarially chosen at each round.
Scalable and Independent Learning of Nash Equilibrium Policies in $n$-Player Stochastic Games with Unknown Independent Chains
Specifically, under no assumptions on the reward functions, we show the proposed algorithm converges in polynomial time in a weaker distance (namely, the averaged Nikaido-Isoda gap) to the set of $\epsilon$-NE policies with arbitrarily high probability.
Online Reinforcement Learning in Markov Decision Process Using Linear Programming
We consider online reinforcement learning in episodic Markov decision process (MDP) with unknown transition function and stochastic rewards drawn from some fixed but unknown distribution.
Local Environment Poisoning Attacks on Federated Reinforcement Learning
However, the federated mechanism also exposes the system to poisoning by malicious agents that can mislead the trained policy.
The Role of Local Steps in Local SGD
Our main contribution is to characterize the convergence rate of Local SGD as a function of $\{H_i\}_{i=1}^R$ under various settings of strongly convex, convex, and nonconvex local functions, where $R$ is the total number of communication rounds.
Faster Convergence of Local SGD for Over-Parameterized Models
For general convex loss functions, we establish an error bound of $\O(1/T)$ under a mild data similarity assumption and an error bound of $\O(K/T)$ otherwise, where $K$ is the number of local steps.
Learning Stationary Nash Equilibrium Policies in $n$-Player Stochastic Games with Independent Chains
We consider a subclass of $n$-player stochastic games, in which players have their own internal state/action spaces while they are coupled through their payoff functions.
Online Learning in Budget-Constrained Dynamic Colonel Blotto Games
In this paper, we study the strategic allocation of limited resources using a Colonel Blotto game (CBG) under a dynamic setting and analyze the problem using an online learning approach.
Maximizing Social Welfare Subject to Network Externalities: A Unifying Submodular Optimization Approach
We consider the problem of allocating multiple indivisible items to a set of networked agents to maximize the social welfare subject to network externalities.
Computer Science and Game Theory Discrete Mathematics Multiagent Systems Systems and Control Systems and Control Optimization and Control
Communication-efficient Decentralized Local SGD over Undirected Networks
Agents have access to $F$ through noisy gradients, and they can locally communicate with their neighbors a network.
Maximizing Convergence Time in Network Averaging Dynamics Subject to Edge Removal
We consider the consensus interdiction problem (CIP), in which the goal is to maximize the convergence time of consensus averaging dynamics subject to removing a limited number of network edges.
Toward Optimal Adversarial Policies in the Multiplicative Learning System with a Malicious Expert
We consider a learning system based on the conventional multiplicative weight (MW) rule that combines experts' advice to predict a sequence of true outcomes.
