Ümit Ulusoy, Mehmet Serdar Güzel and Erkan Bostanci (April 22nd 2020). A Q-Learning-Based Approach for Simple and Multi-Agent Systems, Multi Agent Systems - Strategies and Applications, Ricardo López - Ruiz, IntechOpen, DOI: 10.5772/intechopen.88484.

Mengchun Xie (January 14th 2011). Cooperative Behavior Rule Acquisition for Multi-Agent Systems by Machine Learning, Advances in Reinforcement Learning, Abdelhamid Mellouk, IntechOpen, DOI: 10.5772/13280.

Kyriakos G. Vamvoudakis and Frank L. Lewis (January 14th 2011). Online Gaming: Real Time Solution of Nonlinear Two-Player Zero-Sum Games Using Synchronous Policy Iteration, Advances in Reinforcement Learning, Abdelhamid Mellouk, IntechOpen, DOI: 10.5772/13209. 

New Zealand

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

We propose Stable Emergent Policy (STEP) approximation, a distributed policy iteration scheme to stably approximate decentralized policies for partially observable and cooperative multi-agent systems. STEP offers a novel training architecture, where function approximation is used to learn from action recommendations of a decentralized planning algorithm. Planning is enabled by exploiting a training simulator, which is assumed to be available during centralized learning, and further enhanced by reintegrating the learned policies.


A Distributed Policy Iteration Scheme for Cooperative Multi-Agent Policy Approximation

Downloads

Next from AAMAS 2020

Discrete-to-Deep Supervised Policy Learning

Similar lecture

HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures