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multi-agent-reinforcement-learning

topic11 events
papersTODAY 04:00 UTC

arXiv paper proposes evolutionary framework for multi-agent Q-learning with mean-field feedback

A new arXiv preprint introduces an evolutionary computation approach to multi-agent reinforcement learning in networked populations. The framework combines individual adaptation, local interactions, and shifting environmental conditions through mean-field environmental feedback. The authors frame the work as a way to study how these coupled learning and environment dynamics interact.

multi-agent/reinforcement-learning/research/evolutionary-computation
papersTODAY 04:00 UTC

Multi-agent reinforcement learning framework targets automated related work sections

A new arXiv paper proposes CREW, a collaborative multi-agent reinforcement learning framework for generating the related work section of research papers. The authors aim to reduce the time and effort researchers spend writing these sections, addressing limitations they identify in earlier multi-agent LLM approaches. The work appears in the cs.LG category on arXiv.

agents/research/llm/writing
papersTODAY 04:00 UTC

Multi-Agent RL Approach to Factory Task Assignment and Navigation Tested on Real Robots

A new arXiv paper examines how multi-agent reinforcement learning can be applied to task assignment and navigation for robot fleets in industrial settings. The authors focus on the gap between simulation training and deployment on physical multi-robot systems, a step that remains difficult in practice. The work reports on transferring learned policies from simulated environments to real factory robots.

reinforcement-learning/robotics/multi-agent/sim-to-real
papersTODAY 04:00 UTC

Researchers propose KL-projected natural policy gradient algorithms for Nash equilibrium learning in Markov potential games

A new arXiv paper studies decentralized learning of Nash equilibria in infinite-horizon discounted Markov games where agents only receive bandit feedback. The authors develop KL-projected natural policy gradient methods for both episodic and fully online asynchronous settings, aimed at Markov alpha-potential games. They also discuss applications to Markov congestion games.

reinforcement-learning/game-theory/arxiv/multi-agent
papersTODAY 04:00 UTC

Paper Introduces Robust Communication Method for Multi-Agent Reinforcement Learning

A new arXiv preprint presents a method for making the messages exchanged between agents in multi-agent reinforcement learning both informative and resilient to physical constraints. The work targets distributed intelligence settings where learned communication must stay reliable under real-world limitations. It is listed under both cs.AI and cs.LG.

multi-agent/reinforcement-learning/communication/arxiv
papersTODAY 04:00 UTC

LLM-Assisted Multi-Agent RL Framework Coordinates EV Charging, Stations and Grid

A new arXiv paper proposes combining large language models with multi-agent reinforcement learning to jointly optimize electric vehicle charging scheduling in public charging systems. The approach targets three competing goals at once: driver charging satisfaction, charging station profitability, and stability of the smart grid. It is positioned as a unified optimization method for connected EV infrastructure in IoT settings.

agents/reinforcement-learning/energy/llm
papersSEP 12 04:00 UTC

DRG-MAPPO: Hierarchical Role-Graph Multi-Agent RL for Cooperative Air Combat

A new arXiv paper introduces DRG-MAPPO, a hierarchical multi-agent reinforcement learning method that builds dynamic role graphs to improve tactical coordination among cooperating agents. The authors apply it to cooperative air combat scenarios, where multiple autonomous units must make complex decisions together. The work targets better coordination and role assignment than prior MARL approaches in this domain.

multi-agent/reinforcement-learning/air-combat/research
papersSEP 11 04:00 UTC

arXiv paper proposes method for certifying cooperation in multi-agent tasks

A revised arXiv preprint introduces an approach to generating cooperative multi-agent tasks and certifying when cooperation is actually required. It works within the Laser Learning Environment, a multi-agent path-finding setting where shared rewards alone do not guarantee that agents must collaborate. The method aims to formalize when, how, and whether agents need to cooperate to succeed.

multi-agent/research/arxiv/reinforcement-learning
papersSEP 10 04:00 UTC

ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork

A new arXiv paper presents ROTATE, a training method designed to help agents cooperate effectively with partners they have never seen before, a problem known as ad hoc teamwork. Rather than relying on a pre-built, fixed set of teammate agents followed by a separate coordination stage, the approach uses regret signals to continuously steer the creation of training collaborators. The work addresses generalization in multi-agent reinforcement learning.

multi-agent/reinforcement-learning/generalization/open-ended
papersSEP 10 04:00 UTC

Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning

A new arXiv paper examines permutation surrogate tests, a common tool for estimating directed influence between reinforcement learning agents, by validating them against known ground truth. In two multi-agent settings, a social dilemma and a coordination race, the authors find that non-stationarity in agent behavior undermines these surrogate methods. The study provides diagnostics and corrective approaches to make information-theoretic influence measures more reliable.

multi-agent/reinforcement-learning/information-theory/evaluation
papersSEP 10 04:00 UTC

Multi-agent deep reinforcement learning trains UAV teams for simulated wildfire monitoring

A new study presents a deep reinforcement learning framework that trains multiple drone agents to explore and keep watch over virtual wildfire scenes. Across training, the agents progressively developed consistent and effective surveillance behavior. The research points toward coordinated autonomous aircraft for real-world wildfire response.

reinforcement-learning/agents/drones/research