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Feb 26, 2026 · Artificial Intelligence

How In-Context Co‑Player Inference and LLM‑Driven Evolution Are Redefining Multi‑Agent RL

This article analyzes two recent Google papers—one introducing context‑based co‑player inference for robust multi‑agent cooperation and the other presenting AlphaEvolve, an LLM‑guided evolutionary framework that automatically discovers novel multi‑agent learning algorithms—detailing their methods, experimental findings, and broader implications for AI research.

AlphaEvolveLLM-driven algorithm discoveryPredictive Policy Improvement

0 likes · 11 min read

How In-Context Co‑Player Inference and LLM‑Driven Evolution Are Redefining Multi‑Agent RL

PaperAgent

Feb 25, 2026 · Artificial Intelligence

How Contextual Co-Player Inference Enables Robust Multi-Agent Cooperation

These two recent Google papers advance multi‑agent reinforcement learning: one introduces contextual co‑player inference to achieve robust cooperation without explicit meta‑learning, while the other presents AlphaEvolve, a large‑language‑model‑driven evolutionary framework that automatically discovers novel MARL algorithms such as VAD‑CFR and SHOR‑PSRO.

AI researchCFRLLM-driven algorithm discovery

0 likes · 13 min read

How Contextual Co-Player Inference Enables Robust Multi-Agent Cooperation

Machine Learning Algorithms & Natural Language Processing

Feb 18, 2026 · Artificial Intelligence

Multi-Agent Communication: A Survey from MARL to Emergent Language and Large Language Models

This survey examines the evolution of multi‑agent communication—from early hand‑crafted protocols in MARL, through emergent discrete languages, to recent large‑language‑model‑driven approaches—using a unified "five W" framework to analyze who communicates, what, when, why, and how.

communication protocolsemergent languagelarge language models

0 likes · 19 min read

Multi-Agent Communication: A Survey from MARL to Emergent Language and Large Language Models

AntTech

Jan 16, 2026 · Databases

Can Multi‑Agent Collaboration Automatically Tune Database Parameters with High Efficiency?

The paper presents CMA+DB, a hierarchical multi‑agent framework that automatically tunes database parameters across diverse workloads by combining classification‑based collaboration, layered training, and joint action selection, achieving superior performance, faster convergence, and strong generalization compared with existing tuning methods.

CMA+DBDatabase TuningPerformance Evaluation

0 likes · 9 min read

Can Multi‑Agent Collaboration Automatically Tune Database Parameters with High Efficiency?

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.

Deep LearningLoad-Aware SchedulingResource Optimization

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 LearningPPOTorchRL

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.

AIaction semanticscurriculum learning

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.

auto-biddingmean fieldmulti-agent reinforcement learning

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.

Taobao advertising platformbidding optimizationcooperative game theory

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.

AIRoute PlanningTraffic Prediction

0 likes · 14 min read

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

Alibaba Cloud Developer

Apr 7, 2017 · Artificial Intelligence

How BiCNet Enables Multi‑Agent Cooperation in StarCraft Battles

This article reviews the BiCNet framework, a bidirectional coordination network that lets multiple AI agents learn cooperative strategies in StarCraft micro‑battles, achieving state‑of‑the‑art performance across various combat scenarios and demonstrating broad applicability to real‑world multi‑agent tasks.

BiCNetDeep LearningStarCraft

0 likes · 14 min read

How BiCNet Enables Multi‑Agent Cooperation in StarCraft Battles

Alibaba Cloud Developer

Apr 4, 2017 · Artificial Intelligence

BiCNet: Mastering Multi-Agent Cooperation in StarCraft Battles

The paper introduces BiCNet, a bidirectional coordination network that learns optimal multi‑agent strategies in StarCraft micro‑battles—ranging from collision‑free movement to complex cover attacks and focused fire—outperforming prior state‑of‑the‑art methods and demonstrating scalable potential for real‑world cooperative AI tasks.

BiCNetDeep LearningStarCraft

0 likes · 14 min read

BiCNet: Mastering Multi-Agent Cooperation in StarCraft Battles