arXiv AI

Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning

arXiv:2607. 19117v1 Announce Type: new Abstract: Parameterized action reinforcement learning has shown strong performance in environments requiring both discrete action selection and continuous parameterization.

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

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

arXiv Machine Learning

Aug 4

Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training

arXiv:2608. 02391v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive.

By Zhiyuan Wang, Shengcai Liu, Jiahao Wu, Ning Lu, Hui Ouyang, Shaofeng Zhang, Haoze Lv, Ke Tang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 30

Accelerating Q-learning through Efficient Value-Sharing across Actions

arXiv:2606. 29806v1 Announce Type: cross Abstract: Action-values are foundational to many control algorithms such as Q-learning.

By Prabhat Nagarajan, Brett Daley, Martha White, Marlos C. Machado

reinforcement-learning

arXiv AI

Jun 11

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions

arXiv:2509. 10303v2 Announce Type: replace-cross Abstract: Online reinforcement learning (RL) approaches have demonstrated strong performance on Job Shop Scheduling (JSP) and Flexible JSP (FJSP) problems by learning scheduling policies through direct interaction with simulated environments.

By Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 4

Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling

arXiv:2606. 05021v1 Announce Type: new Abstract: We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm.

By Marc Walden, Jason Liu, Shaashwath Sivakumar, Ryan Liu, Hamza Khan

agents reinforcement-learning benchmarks

arXiv AI

1d ago

ClawGym II: Exploring Black-Box RL on Agent Harness

arXiv:2608. 16798v1 Announce Type: cross Abstract: Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment.

By Huatong Song, Fei Bai, Ming Yang, Renyuan Li, Jia Deng, Jujie He, Zhange Zhang, Daixuan Cheng, Yan Xing, Qi Yun, Xuxing Chen, Danyang Li, Feng Chang, Chuan Hao, Ran Tao, Jian Yang, Bryan Dai, Wayne Xin Zhao, Mingjie Tang, Ji-Rong Wen

llms agents reinforcement-learning

arXiv AI

Jul 7

ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

arXiv:2602. 21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks.

By Xiaoxuan Wang, Han Zhang, Haixin Wang, Yidan Shi, Ruoyan Li, Kaiqiao Han, Chenyi Tong, Haoran Deng, Renliang Sun, Alexander Taylor, Yanqiao Zhu, Jason Cong, Yizhou Sun, Wei Wang

llms agents reinforcement-learning

arXiv AI

Jun 8

CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space

arXiv:2601. 05675v2 Announce Type: replace Abstract: Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI.

By Bingyi Liu, Jinbo He, Haiyong Shi, Enshu Wang, Weizhen Han, Jingxiang Hao, Peixi Wang, Zhuangzhuang Zhang

rag agents diffusion reinforcement-learning robotics benchmarks

arXiv AI

Jun 10

3SPO: State-Score-Supervised Policy Optimization for LLM Agents

arXiv:2606. 09961v1 Announce Type: cross Abstract: Training large language models (LLMs) as autonomous agents via reinforcement learning (RL) has enabled frontier models to achieve superhuman performance in long-horizon tasks.

By Yu Han, Kailing Li, Yang Jiao, Yulin Dai, Yuqian Fu, Linhai Zhuo, Tianwen Qian

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

Jun 2

Coordination Graphs for Constrained Multi-Agent Reinforcement Learning

arXiv:2606. 02337v1 Announce Type: new Abstract: Constrained Multi-agent reinforcement learning (CMARL) faces two intertwined challenges: the joint action space grows exponentially with the number of agents, and additional requirements couple agents in ways that reward structure alone does not capture.

By Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders Jonsson

agents reinforcement-learning

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