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Found 7 papers, 1 papers with code
Paths to Equilibrium in Normal-Form Games
In multi-agent reinforcement learning (MARL), agents repeatedly interact across time and revise their strategies as new data arrives, producing a sequence of strategy profiles.
Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method
Despite the success of generative adversarial networks (GANs) in generating visually appealing images, they are notoriously challenging to train.
Second-Order Mirror Descent: Convergence in Games Beyond Averaging and Discounting
In this paper, we propose a second-order extension of the continuous-time game-theoretic mirror descent (MD) dynamics, referred to as MD2, which provably converges to mere (but not necessarily strict) variationally stable states (VSS) without using common auxiliary techniques such as time-averaging or discounting.
An inexact-penalty method for GNE seeking in games with dynamic agents
We show that these dynamics converge to an epsilon-GNE while satisfying the constraints for all time, not only in steady-state.
Continuous-time Discounted Mirror-Descent Dynamics in Monotone Concave Games
In this paper, we consider concave continuous-kernel games characterized by monotonicity properties and propose discounted mirror descent-type dynamics.
From Game-theoretic Multi-agent Log Linear Learning to Reinforcement Learning
The main focus of this paper is on enhancement of two types of game-theoretic learning algorithms: log-linear learning and reinforcement learning.
On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning
In this paper, we utilize results from convex analysis and monotone operator theory to derive additional properties of the softmax function that have not yet been covered in the existing literature.
