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03/23/2023
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
The aim of this paper is to improve the understanding of the optimizatio...
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06/15/2022
Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games
We study the performance of policy gradient methods for the subclass of ...
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05/27/2022
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
We study the problem of finding the Nash equilibrium in a two-player zer...
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12/17/2021
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems
There are much recent interests in solving noncovnex min-max optimizatio...
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10/21/2021
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes
We consider a discounted cost constrained Markov decision process (CMDP)...
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09/29/2021
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
We study a novel two-time-scale stochastic gradient method for solving o...
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08/26/2021
Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy
We consider the problem of Byzantine fault-tolerance in federated machin...
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04/04/2021
Finite-Time Convergence Rates of Nonlinear Two-Time-Scale Stochastic Approximation under Markovian Noise
We study the so-called two-time-scale stochastic approximation, a simula...
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01/26/2021
Finite Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm
Actor-critic style two-time-scale algorithms are very popular in reinfor...
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11/03/2020
Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance
Two-time-scale stochastic approximation, a generalized version of the po...
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10/28/2020
Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning
Stochastic approximation, a data-driven approach for finding the fixed p...
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09/30/2020
Byzantine Fault-Tolerance in Decentralized Optimization under Minimal Redundancy
This paper considers the problem of Byzantine fault-tolerance in multi-a...
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06/24/2020
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms
Motivated by broad applications in reinforcement learning and federated ...
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03/24/2020
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness
Motivated by broad applications in reinforcement learning and machine le...
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12/23/2019
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation
Motivated by their broad applications in reinforcement learning, we stud...
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07/25/2019
Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation
We study the policy evaluation problem in multi-agent reinforcement lear...
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05/27/2019