一、前 LLM 时代的 multi-agent 系统 1.1 multi-agent RL 问题建模 1.2 multi-agent RL 求解范式 二、协作型的 multi-agent 系统 2.1 协作机制 2.2 对话系统 2.3 控制系统 三、竞争型的 multi-agent 系统 3.1 竞争型的解释及其与协作型的比较 3.2 典型的竞争型的案例 参考资料 在上一篇关于 RAG 的讨论中已...
强化学习(RL)本来就已经是一个比较玄学的领域了,Multi Agent RL是RL中也比较玄学的领域,堪称“玄学的平方”。说实话我并不建议大家去深入钻这个领域,适当涉猎即可。 1.2、Multi Agent用于LLM数据生产与对齐 在LLM相关领域中,我最早看到Multi Agent思路还是用于对齐或其他数据生产,而不是现在这样作为AI Agent运行时的...
This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments. Dealing with multiple agents is inherently more c...
A mechanism for achieving coordination in multi-agent RL through rewarding agents for having causal Influence over other agents actions. Actions that lead to bigger changes in other agents behavior are considered influential and are rewarded. Influence is assessed using counterfactual reasoning. in agent...
KiloBot-MultiAgent-RL This is an experimentation to learn about Swarm Robotics with help of MultiAgent Reinforcement learning. We have used KiloBot as a platform as these are very simple in the actions space and have very high degree of symmetry. The Main inspiration of this project is this ...
However, centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. Multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now the environment becomes...
为了使基于大模型的Agent适合于Multi-Agent的对话,每个Agent都可以进行对话,它们可以接收、响应和响应消息。当配置正确时 ,Agent可以自动与其他代理进行多次对话,或者在某些对话轮次中请求人工输入,从而通过人工反馈形成RLHF。可对话的Agent设计利用了LLM通过聊天获取反馈并取得进展的强大能力,还允许以模块化的方式组合LLM的...
the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techn...
单个agent的主要区别在于,状态转移要取决于多个agent的动作。 多个agent,收敛一般是达到纳什均衡。 5.3 中心化和去中心化 5.3.1 完全去中心化 各自训练,各自执行 5.3.2 完全中心化 中央训练,中央执行 Alt text 缺点:时间慢,agent慢 5.3.3 中央训练,各自执行 ...
为了使基于大模型的Agent适合于Multi-Agent的对话,每个Agent都可以进行对话,它们可以接收、响应和响应消息。当配置正确时 ,Agent可以自动与其他代理进行多次对话,或者在某些对话轮次中请求人工输入,从而通过人工反馈形成RLHF。可对话的Agent设计利用了LLM通过聊天获取反馈并取得进展的强大能力,还允许以模块化的方式组合LLM的...