Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 15 October 2020Accepted: 16 August 2021Published online: 28 2021Keywordsmean-field control, multi-agent reinforcement learning, Q-learning, cooperative games, dynamic programming principleAMS Subject Headings49N80, 68Q32, 68T05, 90C40Publication DataISSN (online): 2577-01...

Multi-agent reinforcement learning (MARL) is often modeled using the framework of Markov games (also called stochastic or dynamic games). Most existing literature on MARL concentrates zero-sum but not applicable to general-sum games. It known that best response dynamics in are a contraction. Therefore, different equilibria can have values. Moreover, Q-function sufficient completely characterize...

Multiagent decision-making in partially observable environments is usually modelled as either an extensive-form game (EFG) theory or a stochastic (POSG) multiagent reinforcement learning (MARL). One issue with the current situation that while most practical problems can be both formalisms, relationship of two models unclear, which hinders transfer ideas between communities. A second EFGs have r...

We propose a novel formulation of the “effectiveness problem” in communications, put forth by Shannon and Weaver their seminal work “The Mathematical Theory Communication”, considering multiple agents communicating over noisy channel order to achieve better coordination cooperation multi-agent reinforcement learning (MARL) framework. Specifically, we consider partially observable Markov decisio...

Abstract Multiagent reinforcement learning (MARL) has been used extensively in the game environment. One of main challenges MARL is that environment agent system dynamic, and other agents are also updating their strategies. Therefore, modeling opponents’ process adopting specific strategies to shape an effective way obtain better training results. Previous studies such as DRON, LOLA SOS approxi...

Abstract The rapidly evolving urban air mobility (UAM) develops the heavy demand for public transport tasks and poses great challenges to safe efficient operation in low-altitude airspace. In this paper, conflict is managed strategic phase with multi-agent reinforcement learning (MARL) dynamic environments. To enable operation, aircraft flight performance integrated into process of multi-resolu...

Rhythmical alternations between limestone and marls characterize the Pabdeh Formation, southwestern Iran. Three intervals of these rhythmites were studied using sedimentary, petrography and geochemical parameters analysis, to unravel the possible mechanisms responsible for the origin of these rhythmites. The microfacies analysis reflects calm deep-water sedimentation that were interrupted by sp...