arXiv AI July 22, 2026 By Yamato Takahagi, Gentoku Nakasone, Yoshinari Motokawa, Toshiharu Sugawara

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents

arXiv:2607. 18719v1 Announce Type: cross Abstract: This study proposes a learning method for multi-agent systems that allows agents to be controlled through human manager instructions after learning and enables uninstructed agents to implicitly complement the overall work based on the actions of other agents.

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

arXiv AI

Jun 3

Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning

arXiv:2511. 02304v2 Announce Type: replace-cross Abstract: We study learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution.

By Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte, Ameesh Shah, Hanna Krasowski, Sanjit A. Seshia

agents reinforcement-learning

arXiv Machine Learning

Jul 1

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

arXiv:2507. 23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity.

By Tommaso Marzi, Cesare Alippi, Andrea Cini

agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 6

State2State: Environment-Derived Mid-Training for LLM Agents

arXiv:2608. 04934v1 Announce Type: cross Abstract: Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers.

By Xuanyu Lei, Yiqi Zhu, Chenliang Li, Kaiming Liu, Peng Li, Ming Yan, Jieping Ye, Ya-Qin Zhang, Yang Liu

llms agents reinforcement-learning robotics fine-tuning

arXiv AI

Jun 2

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning

arXiv:2605. 18077v2 Announce Type: replace Abstract: Communication is a key component in multi-agent reinforcement learning (MARL) for mitigating partial observability, yet prior approaches often rely on inefficient information exchange or fail to transmit sufficient state information.

By Sangjun Bae, Yisak Park, Sanghyeon Lee, Seungyul Han

llms agents reinforcement-learning benchmarks

Hugging Face Trending Papers

Aug 5

State2State: Environment-Derived Mid-Training for LLM Agents

Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers. Though effective, both remain bottlenecked by externally specified tasks and supervision signals, limiting the scalability and diversity of agent training.

llms agents reinforcement-learning robotics fine-tuning

arXiv AI

Jun 16

Learning to Share: Selective Memory for Efficient Parallel Agentic Systems

arXiv:2602. 05965v2 Announce Type: replace-cross Abstract: Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results.

By Joseph Fioresi, Parth Parag Kulkarni, Ashmal Vayani, Song Wang, Mubarak Shah

agents reinforcement-learning benchmarks

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Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning

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State2State: Environment-Derived Mid-Training for LLM Agents

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning

State2State: Environment-Derived Mid-Training for LLM Agents

Learning to Share: Selective Memory for Efficient Parallel Agentic Systems