Integration of Multi-agent Reinforcement Learning (MARL) and Large Language Models (LLMs) in Agentic AI Systems: A Narrative Review

A Survey on Reinforcement Learning Optimization Methods for Multi-Agent Collaboration of Large Language Models

The integration of reinforcement learning (RL) into the optimization of multi-agent collaboration for Large Language Models (LLMs) is an important combination of two advanced areas, Multi-Agent Systems (MAS) and LLMs. This paper thoroughly examines the main approaches, evaluation standards, recent progress and existing...

Large Language Model (LLM) as an Excellent Reinforcement Learning Researcher in both Single-Agent and Multi-Agent Scenarios

A Self-Evolutional single-agent/multi-agent Reinforcement Learning (SE-RL) framework that utilizes a Large Language Model (LLM) to design various RL algorithm modules, such as agent model design, reward function, profiling, communication, and state imagination, by leveraging the LLM generating module output or code.

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly adapt to new tasks/environments using a bi-level optimization mechanism. However, existing...

Large Language Models for Self-Adaptive Systems: Feedback-Loop Integration and Agentic Adaptation

A Survey on Value Alignment in Agentic AI Systems

With the evolution of artificial intelligence (AI) paradigms towards agentic AI, the widespread integration of large language models (LLMs) enhances system capabilities while also introducing situational risks and challenges of value misalignment, making value alignment in agentic AI systems a critical issue. This pape...

Robot Intelligent Decision-making: From Rule-based Systems to Deep Learning and Multi-agent Methods

The results show that although the rule-based method provides interpretability and security, the deep learning method performs well in dealing with high-dimensional sensory input and an unstructured environment and Multi-Agent Reinforcement Learning shows great potential in the application of cooperative robots.