Decentralized Multi|agent Reinforcement Learning with Shared ...

Xueguang Lyu: Understanding Centralized Critics - YouTube

decentralized manner, has become a popular approach in Multi-Agent Reinforcement Learning (MARL) ... common intuition: critic ...

Efficient and scalable reinforcement learning for large-scale network ...

Here we develop a model-based decentralized policy optimization framework, which can be efficiently deployed in multi-agent systems.

Multi-agent deep reinforcement learning: a survey

When agents face a cooperative task with a shared reward function, the POMG is then known as decentralized Partially Observable Markov decision ...

Centralised Control or Decentralised Coordination? - Medium

In a centralised approach to train Multi-Agent Reinforcement Learning (MARL), all agents share a single central decision-maker or controller.

Track: RL: Multi-agent - ICML 2025

Many advances in cooperative multi-agent reinforcement learning (MARL) are based on two common design principles: value decomposition and parameter sharing. A ...

A New Framework for Multi-Agent Reinforcement Learning

However, the common strategy for every agent to learn ... Multi-agent reinforcement learning as a rehearsal for decentralized planning.

Deep Decentralized Reinforcement Learning for Cooperative Control

We propose new, modular deep decentralized Multi-Agent Reinforcement Learning mechanisms to account for these challenges.

Multi-Agent Reinforcement Learning as a Rehearsal for ... - CORE

Multi-Agent Reinforcement Learning as a Rehearsal for Decentralized ... learning algorithm such as RLaR due to the shared lack of a priori ...

Decentralized Multi-agent Reinforcement Learning System - LEMUR

Current SOTA decentralized multi-agent reinforcement learning (Dec-MARL) algorithms mostly assume that, at least at training stage, agents are able to obtain ...

Provably Efficient Multi-Agent Reinforcement Learning with Fully ...

We investigate the benefits to performance in MARL when exploration is fully decentralized. Specifically, we consider a class of online, episodic, tabular Q- ...

Cross-agent Pooling in decentralized Multi-agent execuation - RLlib

In simple words, I have five shared-parameter agents and I want to concat a team-wise feature, which is maxpooled on all agents' FC outputs, to ...

Decentralized multi-agent reinforcement learning based on best ...

In order to exploit results from single- agent RL, a common paradigm in MARL is centralized learning with decentralized execution.

A learning agent that acquires social norms from public sanctions in ...

It learns by applying a decentralized multi-agent reinforcement learning algorithm. ... A multi-agent reinforcement learning model of common ...

Integration of Decentralized Graph-Based Multi-Agent ... - MDPI

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in ...

single agent vs multiple agent reinforcement learning

A key difference between RL and MARL arises when you consider that other agents are strategic and their behaviour is adaptive.

Learning Decentralized Policies in Multiagent Systems - YouTube

Na Li (Harvard University) https://simons.berkeley.edu/talks/tbd-400 Multi-Agent Reinforcement Learning and Bandit Learning Multiagent ...

An Introduction to Multi-Agent Reinforcement Learning - MATLAB

With a decentralized architecture, no information is shared between the agents. They are completely on their own for learning. And this has ...

Resilient multi-agent RL: introducing DQ-RTS for distributed ... - Nature

This paper proposes DQ-RTS, a novel decentralized Multi-Agent Reinforcement Learning algorithm designed to address challenges posed by non-ideal communication.

Decentralized Reinforcement Learning - Javatpoint

... multi-agent robotics, decentralized autonomous systems In large distributed systems. ... Privacy Concerns: It may not be desirable to share information ...

Xin Guo: Mean-field multi-agent reinforcement learning - YouTube

Xin Guo: Mean-field multi-agent reinforcement learning: a decentralized network approach ... Share. Save. Report. 50:34. Go ...