arXiv AI July 22, 2026 By Ubayd Ali Bapoo, Clement N Nyirenda

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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 Machine Learning

Aug 24

Decoupling Policy Extraction for Offline Reinforcement Learning

arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...

By Xuyao Lin, Yixiang Shan, Jinru Duan, Tao Yang, Xinyu Zhao, Runyu Lei, Yiming Zhao, Jiaxin Fan, Zongbao Feng, Peng Jia

rag reinforcement-learning

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 Machine Learning

Sep 4

LLM-Guided Reinforcement Learning for Adaptive NPC Behavior in Multi-Agent Combat Games

The paper explores a runtime strategy-selection framework where a large language model (LLM) guides a pre‑trained reinforcement learning (RL) policy for non‑player characters (NPCs) in a Unity combat game without altering the underlying policy. Five NPC agents sharing a PPO policy were compared in a baseline setup and an LLM‑augmented setup, where a locally hosted Mistral 7B model assigns one of four tactical tags every five seconds based on live game state. Across 600 episodes against three scripted opponents, the LLM‑augmented agents more than doubled their win rate against a Balanced opponent, improved performance against an Evasive opponent, but struggled against an Aggressive opponent due to over‑reliance on encirclement; analysis of 2,430 strategy selections revealed limited zero‑shot differentiation with the model favoring Surround in 83.8% of cases.

By Hrithika Deepu Nair, Kayvan Karim

llms agents reinforcement-learning

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Related stories

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

Decoupling Policy Extraction for Offline Reinforcement Learning

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

Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training

Accelerating Q-learning through Efficient Value-Sharing across Actions

LLM-Guided Reinforcement Learning for Adaptive NPC Behavior in Multi-Agent Combat Games