Multi-Agent Reinforcement Learning

JD Tech

Apr 8, 2025 · Artificial Intelligence

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Advertising Systems

The article presents MaRCA, a multi‑agent reinforcement learning framework that models user value, compute consumption, and action reward to allocate limited computation resources across the entire advertising recommendation pipeline, achieving higher ad revenue while keeping system load stable under fluctuating traffic and diverse request values.

Advertising SystemsDeep LearningLoad-Aware Scheduling

0 likes · 16 min read

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Advertising Systems

Python Programming Learning Circle

Sep 10, 2024 · Artificial Intelligence

Using TorchRL to Implement Multi‑Agent PPO for MARL

This tutorial explains how to set up a multi‑agent reinforcement learning (MARL) environment with VMAS, install required dependencies, configure PPO hyper‑parameters, build policy and critic networks, collect data with TorchRL, and run a training loop to train agents for coordinated navigation tasks.

Deep LearningMulti-Agent Reinforcement LearningPPO

0 likes · 10 min read

Using TorchRL to Implement Multi‑Agent PPO for MARL

DataFunTalk

Aug 24, 2023 · Artificial Intelligence

Multi-Agent Decision Large Models: Challenges, Action Semantic Networks, Permutation Invariance/Equivariance, and Automated Curriculum Learning

This talk outlines the fundamental challenges of multi‑agent decision large models, introduces three core design priors—action semantic networks, permutation invariance/equivariance, and cross‑task automated curriculum learning— and demonstrates how these concepts improve performance across diverse environments such as StarCraft, Neural‑MMO, and SMAC.

AIMulti-Agent Reinforcement Learningaction semantics

0 likes · 12 min read

Multi-Agent Decision Large Models: Challenges, Action Semantic Networks, Permutation Invariance/Equivariance, and Automated Curriculum Learning

Alimama Tech

Mar 9, 2022 · Artificial Intelligence

Multi-Agent Auto-bidding (MAAB): A Framework for Distributed Automatic Bidding in Online Advertising

The paper introduces MAAB, a scalable multi‑agent reinforcement‑learning framework for online ad bidding that uses temperature‑regularized credit assignment, adaptive threshold agents, and mean‑field clustering to balance individual advertiser utility, platform revenue, and overall social welfare in competitive auction environments.

Multi-Agent Reinforcement Learningauto-biddingmean field

0 likes · 28 min read

Multi-Agent Auto-bidding (MAAB): A Framework for Distributed Automatic Bidding in Online Advertising

Alimama Tech

Oct 13, 2021 · Artificial Intelligence

Multi-Agent Cooperative Bidding Game Framework for Multi-Objective Optimization in Online Advertising

The paper presents MACG, a multi‑agent cooperative bidding game that integrates a global objective with individual advertiser goals, derives optimal bidding formulas, employs a strategy network and evolutionary search to tune parameters, and demonstrates over‑5% metric gains and stable 15‑day performance in Taobao’s online advertising platform.

Multi-Agent Reinforcement LearningReal-Time BiddingTaobao advertising platform

0 likes · 18 min read

Multi-Agent Cooperative Bidding Game Framework for Multi-Objective Optimization in Online Advertising

Amap Tech

Mar 5, 2021 · Artificial Intelligence

AI Applications in Mobility: Route Planning, ETA Prediction, Dynamic Event Mining, and Global Scheduling

The article surveys Amap’s AI‑driven mobility solutions—from personalized, multi‑objective route planning using Cell‑Based Routing and bias‑aware sorting, through spatio‑temporal ETA prediction and lightweight BERT‑based traffic‑event mining, to rapid POI freshness updates and a future global scheduling system that coordinates vehicles and signals via multi‑agent reinforcement learning.

AIMulti-Agent Reinforcement LearningRoute Planning

0 likes · 14 min read

AI Applications in Mobility: Route Planning, ETA Prediction, Dynamic Event Mining, and Global Scheduling