An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arXiv preprint arXiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
[HTML][HTML] AutoML: A systematic review on automated machine learning with neural architecture search
Abstract AutoML (Automated Machine Learning) is an emerging field that aims to automate
the process of building machine learning models. AutoML emerged to increase productivity …
the process of building machine learning models. AutoML emerged to increase productivity …
On gradient descent ascent for nonconvex-concave minimax problems
We consider nonconvex-concave minimax problems, $\min_ {\mathbf {x}}\max_ {\mathbf
{y}\in\mathcal {Y}} f (\mathbf {x},\mathbf {y}) $, where $ f $ is nonconvex in $\mathbf {x} $ but …
{y}\in\mathcal {Y}} f (\mathbf {x},\mathbf {y}) $, where $ f $ is nonconvex in $\mathbf {x} $ but …
Independent policy gradient methods for competitive reinforcement learning
C Daskalakis, DJ Foster… - Advances in neural …, 2020 - proceedings.neurips.cc
We obtain global, non-asymptotic convergence guarantees for independent learning
algorithms in competitive reinforcement learning settings with two agents (ie, zero-sum …
algorithms in competitive reinforcement learning settings with two agents (ie, zero-sum …
Near-optimal algorithms for minimax optimization
This paper resolves a longstanding open question pertaining to the design of near-optimal
first-order algorithms for smooth and strongly-convex-strongly-concave minimax problems …
first-order algorithms for smooth and strongly-convex-strongly-concave minimax problems …
Solving a class of non-convex min-max games using iterative first order methods
Recent applications that arise in machine learning have surged significant interest in solving
min-max saddle point games. This problem has been extensively studied in the convex …
min-max saddle point games. This problem has been extensively studied in the convex …
The complexity of constrained min-max optimization
C Daskalakis, S Skoulakis, M Zampetakis - Proceedings of the 53rd …, 2021 - dl.acm.org
Despite its important applications in Machine Learning, min-max optimization of objective
functions that are nonconvex-nonconcave remains elusive. Not only are there no known first …
functions that are nonconvex-nonconcave remains elusive. Not only are there no known first …
Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning
Min–max problems have broad applications in machine learning, including learning with
non-decomposable loss and learning with robustness to data distribution. Convex–concave …
non-decomposable loss and learning with robustness to data distribution. Convex–concave …
Efficient algorithms for smooth minimax optimization
KK Thekumparampil, P Jain… - Advances in Neural …, 2019 - proceedings.neurips.cc
This paper studies first order methods for solving smooth minimax optimization problems
$\min_x\max_y g (x, y) $ where $ g (\cdot,\cdot) $ is smooth and $ g (x,\cdot) $ is concave for …
$\min_x\max_y g (x, y) $ where $ g (\cdot,\cdot) $ is smooth and $ g (x,\cdot) $ is concave for …
Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O (1/k^ 2) Rate on Squared Gradient Norm
In this work, we study the computational complexity of reducing the squared gradient
magnitude for smooth minimax optimization problems. First, we present algorithms with …
magnitude for smooth minimax optimization problems. First, we present algorithms with …