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Found 8 papers, 2 papers with code
Finite-sample Guarantees for Nash Q-learning with Linear Function Approximation
Nash Q-learning may be considered one of the first and most known algorithms in multi-agent reinforcement learning (MARL) for learning policies that constitute a Nash equilibrium of an underlying general-sum Markov game.
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
Second, we introduce a new analysis of optimization based on Restricted Strong Convexity (RSC) which holds as long as the squared norm of the average gradient of predictors is $\Omega(\frac{\text{poly}(L)}{\sqrt{m}})$ for the square loss.
Discrete State-Action Abstraction via the Successor Representation
While the difficulty of reinforcement learning problems is typically related to the complexity of their state spaces, Abstraction proposes that solutions often lie in simpler underlying latent spaces.
One Policy is Enough: Parallel Exploration with a Single Policy is Near-Optimal for Reward-Free Reinforcement Learning
Although parallelism has been extensively used in reinforcement learning (RL), the quantitative effects of parallel exploration are not well understood theoretically.
A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows
Much recent interest has focused on the design of optimization algorithms from the discretization of an associated optimization flow, i. e., a system of differential equations (ODEs) whose trajectories solve an associated optimization problem.
Distributed Wasserstein Barycenters via Displacement Interpolation
Consider a multi-agent system whereby each agent has an initial probability measure.
Distributed and time-varying primal-dual dynamics via contraction analysis
In this note, we provide an overarching analysis of primal-dual dynamics associated to linear equality-constrained optimization problems using contraction analysis.
Distributionally Robust Formulation and Model Selection for the Graphical Lasso
As a consequence of this formulation, the radius of the Wasserstein ambiguity set is directly related to the regularization parameter in the estimation problem.
