arXiv AI August 13, 2026 By Zijian Zhao, Sen Li

Is Per-Agent Policy Composition Safe? Rethinking Successor-Feature Transfer in Cooperative Multi-Agent Reinforcement Learning

arXiv:2608. 11658v1 Announce Type: cross Abstract: Many reinforcement learning systems, from fleet management to traffic signal control, must serve an objective that changes dynamically after deployment, and retraining a policy for each new objective is prohibitively expensive.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 15

Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning

arXiv:2606. 14130v1 Announce Type: new Abstract: Safe coordination problems surface in multi-agent reinforcement learning when global safety cannot be enforced by any agent unilaterally: the admissibility of one agent's action may depend on the dynamics of other agents.

By Omar Adalat, Edwin Hamel-De le Court, Francesco Belardinelli

agents reinforcement-learning safety

arXiv AI

Jun 30

ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning

arXiv:2606. 30072v1 Announce Type: new Abstract: Cooperative tasks in Multi-Agent Reinforcement Learning (MARL) require agents to collectively maximize a shared return.

By Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara, Scott Sanner, Pascal Poupart, Jongmin Lee, Kee-Eung Kim

agents reinforcement-learning robotics

arXiv AI

Aug 10

IB-RL: Isolated Bilateral Reinforcement Learning for Strategic Dialogue Agents

arXiv:2608. 06735v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved strong results in improving large language models (LLMs) on tasks with stationary, verifiable rewards, such as mathematical reasoning and code execution.

By Senhao Wang, Chenghao Cai, Haitao Hu, Mingxing Huang, Xingguang Wang, Wenhao Li, Zecheng Lin

llms agents reinforcement-learning

arXiv Machine Learning

Jun 25

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning

arXiv:2606. 25526v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning assumes each agent shares the same reward function and can be trained effectively using the Trust Region framework of single-agent.

By Bang Giang Le, Viet Cuong Ta

agents reinforcement-learning benchmarks

arXiv AI

Jul 23

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review

arXiv:2507. 10142v2 Announce Type: replace Abstract: Multi-Agent Reinforcement Learning (MARL) has achieved strong performance in simulated benchmarks, yet real deployments often violate the assumptions under which algorithms are designed and evaluated.

By Siyi Hu, Mohamad A Hady, Jianglin Qiao, Jimmy Cao, Mahardhika Pratama, Ryszard Kowalczyk

agents reinforcement-learning benchmarks

arXiv AI

Jul 28

Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus

arXiv:2604. 13472v2 Announce Type: replace-cross Abstract: Cooperative multi-agent reinforcement learning (MARL) is widely used to address large joint observation and action spaces by decomposing a centralized control problem into multiple interacting agents.

By Zijian Zhao, Jing Gao, Sen Li

llms agents diffusion reinforcement-learning benchmarks

Related stories

Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning

ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learning

IB-RL: Isolated Bilateral Reinforcement Learning for Strategic Dialogue Agents

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review

Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus