In various real-world scenarios, interactions among agents often resemble the
dynamics of general-sum games, where each agent strives to optimize its own
utility. Despite the ubiquitous relevance of such settings, decentralized
machine learning algorithms have struggled to find equilibria that maximize
individual utility while preserving social welfare. In this paper we introduce
Learning with Opponent Q-Learning Awareness (LOQA), a novel, decentralized
reinforcement learning algorithm tailored to optimizing an agent's individual
utility while fostering cooperation among adversaries in partially competitive
environments. LOQA assumes the opponent samples actions proportionally to their
action-value function Q. Experimental results demonstrate the effectiveness of
LOQA at achieving state-of-the-art performance in benchmark scenarios such as
the Iterated Prisoner's Dilemma and the Coin Game. LOQA achieves these outcomes
with a significantly reduced computational footprint, making it a promising
approach for practical multi-agent applications.

本文介绍了一种名为 LOQA 的分布式强化学习算法，用于在部分竞争环境中优化代理个体效用并促进对手之间的合作，在统一代理应用中取得了良好的性能。

LOQA：带有对手 Q 学习意识的学习

LOQA: Learning with Opponent Q-Learning Awareness

With the rapid advancement of Large Language Models (LLMs), significant
progress has been made in multi-agent applications. However, the complexities
in coordinating agents' cooperation and LLMs' erratic performance pose notable
challenges in developing robust and efficient multi-agent applications. To
tackle these challenges, we propose AgentScope, a developer-centric multi-agent
platform with message exchange as its core communication mechanism. Together
with abundant syntactic tools, built-in resources, and user-friendly
interactions, our communication mechanism significantly reduces the barriers to
both development and understanding. Towards robust and flexible multi-agent
application, AgentScope provides both built-in and customizable fault tolerance
mechanisms while it is also armed with system-level supports for multi-modal
data generation, storage and transmission. Additionally, we design an
actor-based distribution framework, enabling easy conversion between local and
distributed deployments and automatic parallel optimization without extra
effort. With these features, AgentScope empowers developers to build
applications that fully realize the potential of intelligent agents. We have
released AgentScope at this https URL, and hope
AgentScope invites wider participation and innovation in this fast-moving
field.

AgentScope 是一个开发人员中心的多代理平台，具有消息交换作为核心通信机制，通过丰富的句法工具、内置资源和用户友好的交互，显著降低了开发和理解的障碍，提供内置和可定制的容错机制，以及多模态数据生成、存储和传输的系统级支持，同时还提供了一个基于角色的分发框架，使本地和分布式部署之间易于转换和自动并行优化，从而使开发人员能够构建充分发挥智能代理潜力的应用程序。

AgentScope 多智能体平台：灵活而强大

AgentScope: A Flexible yet Robust Multi-Agent Platform

The prevalence of multi-agent applications pervades various interconnected
systems in our everyday lives. Despite their ubiquity, the integration and
development of intelligent decision-making agents in a shared environment pose
challenges to their effective implementation. This survey delves into the
domain of multi-agent systems (MAS), placing a specific emphasis on unraveling
the intricacies of learning optimal control within the MAS framework, commonly
known as multi-agent reinforcement learning (MARL). The objective of this
survey is to provide comprehensive insights into various dimensions of MAS,
shedding light on myriad opportunities while highlighting the inherent
challenges that accompany multi-agent applications. We hope not only to
contribute to a deeper understanding of the MAS landscape but also to provide
valuable perspectives for both researchers and practitioners. By doing so, we
aim to facilitate informed exploration and foster development within the
dynamic realm of MAS, recognizing the need for adaptive strategies and
continuous evolution in addressing emerging complexities in MARL.

该论文调查了多智能体系统中学习最优控制的复杂性，旨在深入理解多智能体应用的机遇和挑战，为研究人员和从业者提供有价值的观点，以促进该领域的发展。

多智能体强化学习：综述

Multi-agent Reinforcement Learning: A Comprehensive Survey

This paper introduces epistemic graphs as a generalization of the epistemic
approach to probabilistic argumentation. In these graphs, an argument can be
believed or disbelieved up to a given degree, thus providing a more
fine--grained alternative to the standard Dung's approaches when it comes to
determining the status of a given argument. Furthermore, the flexibility of the
epistemic approach allows us to both model the rationale behind the existing
semantics as well as completely deviate from them when required. Epistemic
graphs can model both attack and support as well as relations that are neither
support nor attack. The way other arguments influence a given argument is
expressed by the epistemic constraints that can restrict the belief we have in
an argument with a varying degree of specificity. The fact that we can specify
the rules under which arguments should be evaluated and we can include
constraints between unrelated arguments permits the framework to be more
context--sensitive. It also allows for better modelling of imperfect agents,
which can be important in multi--agent applications.

该论文介绍了认识图作为概率论证的普遍化，它可以更好地建模攻击和支持关系，包括不支持和不攻击关系，同时允许更多情境敏感性和完善代理建模。