文章链接:Multi-Agent Reinforcement Learning is a Sequence Modeling Problem 文章总结 背景 这篇文章着眼于多智能体强化学习(MARL)的问题,希望通过引入序列模型(SM)来解决这一领域的挑战。 创新点 提出了一种新的解决协作MARL问题的通用框架,将其统一为类似Transformer的编码器-解码器模型。 利用多智能体优势分解定理...
在多智能体强化学习(Reinforcement Learning, RL)的领域中,通信扮演着协调行动的关键角色。然而,培养一种高效的、通用的交流语言构成了一项极具挑战性的任务,尤其是在去中心化的设置中,这种挑战变得更加突出。 (二)解决思路 受到使用数据样本的不同“视图”进行表示学习的文献(Bachman et al., 2019)的启发,作者将...
多智能体强化学习算法MFMARL（Mean Field Multi-Agent Reinforcement Learning）由伦敦大学学院教授汪军提出。该算法主要针对大规模多智能体强化学习问题，通过引入平均场论的思想，简化智能体数量带来的模型空间增大问题。MFMARL算法的实现包括两个主要部分：MF-Q与MF-AC，是对Q-learning和AC算法的改进。理论...
We propose a novel adaptive reinforcement learning (RL) procedure for multi-agent non-cooperative repeated games. Most existing regret-based algorithms only use positive regrets in updating their learning rules. In this paper, we adopt both positive and negative regrets in reinforcement learning to ...
We introduce a hierarchical multi-agent reinforcement learning (RL) framework, and propose a hierarchical multi-agent RL algorithm called Cooperative HRL . In this framework, agents are cooperative and homogeneous (use the same task decomposition). Learning is decentralized, with each agent learning ...
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of...
The dynamics between agents and the environment are an important component of multi-agent Reinforcement Learning (RL), and learning them provides a basis for decision making. However, a major challenge in optimizing a learned dynamics model is the accumulation of error when predicting multiple steps...
apireinforcement-learninggymgymnasiummultiagent-reinforcement-learningmulti-agent-reinforcement-learning UpdatedDec 4, 2024 Python pytorch/rl Star2.4k Code Issues Pull requests Discussions A modular, primitive-first, python-first PyTorch library for Reinforcement Learning. ...
深度强化学习(Multi-Agent Deep Reinforcement Learning, MADRL)是强化学习(Reinforcement Learning, RL)和深度学习(Deep Learning, DL)的交叉领域,其中涉及多个智能体(agent)同时在环境中学习和交互。它尝试解决多智能体系统中的协调、竞争、通信等问题。与单智能体强化学习不同,多智能体系统中的智能体可能有不同的目...
[RL paper.6]An Overview of Multi-agent Reinforcement Learning from Game Theory Perspective 【论文笔记】 近年来,多智能体强化学习(MARL)在解决博弈问题上取得了重要进展。然而在学术上缺少一个涵盖现代MARL在博弈论方面的方法理论基础和最新进展总结的完备概述。本文就来提供一个这样的综述。