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Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization
To address the special optimization problem, we propose a coupled distributed stochastic approximation algorithm, in which every agent updates the current beliefs of its unknown parameter and decision variable by stochastic approximation method; and then averages the beliefs and decision variables of its neighbors over network in consensus protocol.
Distributed Fractional Bayesian Learning for Adaptive Optimization
This paper considers a distributed adaptive optimization problem, where all agents only have access to their local cost functions with a common unknown parameter, whereas they mean to collaboratively estimate the true parameter and find the optimal solution over a connected network.
Online Parameter Identification of Generalized Non-cooperative Game
This work studies the parameter identification problem of a generalized non-cooperative game, where each player's cost function is influenced by an observable signal and some unknown parameters.
No-regret learning for repeated non-cooperative games with lossy bandits
This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback.
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning
This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL), where agents over a communication network aim to find the optimal policy to maximize the average of all agents' local returns.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Online Convex Optimization Over Erdos-Renyi Random Networks
The regret bounds scaling with respect to $T$ match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problems, e. g., $\mathcal{O}(\sqrt{T}) $ and $\mathcal{O}(\ln(T)) $ regrets are established for convex and strongly convex losses with full gradient feedback and two-points information, respectively.
