The key component of this work is a method for computing efficiently the best strategy for a team, given an ap- proximate factored value function which is a ...
We present a new method for learning good strategies in zero-sum Markov games in which each side is composed of multiple agents col- laborating against an ...
We present a new method for learning good strategies in zero-sum Markov games in which each side is composed of multiple agents collaborating against an ...
Jan 1, 2002 · We present a new method for learning good strategies in zero-sum Markov games in which each side is composed of multiple agents ...
This paper investigates value function approx imation in the context of zero-sum Markov games, which can be viewed as a generalization.
In this work, we develop provably efficient reinforcement learning algorithms for two-player zero-sum Markov games with simultaneous moves.
Missing: Team Factored
This paper describes a set of reinforcement-learning algorithms based on estimating value functions and presents convergence theorems for these algorithms.
Apr 2, 2024 · This paper makes progress toward learning Nash equilibria in two-player, zero-sum Markov games from offline data.
Missing: Team | Show results with:Team
Focusing on two-player zero-sum imperfect-information games, we show how to obtain optimal value functions and prove that public information provides both ...
Sep 6, 2024 · This paper investigates the design of decentralized learning algorithms for general-sum Markov games, aiming to provide provable guarantees of ...