actor-attention-critic for multi-agent reinforcement learning
时间: 2023-05-01 17:02:48 浏览: 118
Actor-Attention-Critic是一种用于多智能体强化学习的技术,其由三个主要组件构成,分别是演员(Actor)、注意力(Attention)和评论家(Critic)。演员用于根据当前状态选择一个行为,并将其传递给注意力网络,注意力网络帮助演员在多个智能体之间进行关注,评论者则根据演员选择的行为和当前状态计算回报,并用于指导智能体的决策。该技术可用于解决多智能体系统中的协调问题。
相关问题
a multi-agent actor-critic framework是什么意思
"多智能体演员评论家框架"(multi-agent actor-critic framework)是一种用于解决多智能体强化学习问题的方法。在强化学习中,演员评论家(actor-critic)方法是一种组合了策略学习和值函数学习的技术。
在多智能体环境中,每个智能体都有自己的策略和值函数。演员(actor)根据当前的状态选择动作,评论家(critic)评估该动作的价值。演员根据评论家的反馈来更新策略,以使得智能体能够在环境中获得更好的回报。这种框架允许不同智能体之间相互影响和合作,以最大化整体的回报。
因此,"多智能体演员评论家框架"是一种结合了多智能体强化学习、策略学习和值函数学习的方法,用于解决多智能体环境中的问题。
development of multi-agent reinforcement learning
Multi-agent reinforcement learning (MARL) is a subfield of reinforcement learning (RL) that involves multiple agents learning simultaneously in a shared environment. MARL has been studied for several decades, but recent advances in deep learning and computational power have led to significant progress in the field.
The development of MARL can be divided into several key stages:
1. Early approaches: In the early days, MARL algorithms were based on game theory and heuristic methods. These approaches were limited in their ability to handle complex environments or large numbers of agents.
2. Independent Learners: The Independent Learners (IL) algorithm was proposed in the 1990s, which allowed agents to learn independently while interacting with a shared environment. This approach was successful in simple environments but often led to convergence issues in more complex scenarios.
3. Decentralized Partially Observable Markov Decision Process (Dec-POMDP): The Dec-POMDP framework was introduced to address the challenges of coordinating multiple agents in a decentralized manner. This approach models the environment as a Partially Observable Markov Decision Process (POMDP), which allows agents to reason about the beliefs and actions of other agents.
4. Deep MARL: The development of deep learning techniques, such as deep neural networks, has enabled the use of MARL in more complex environments. Deep MARL algorithms, such as Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG), have achieved state-of-the-art performance in many applications.
5. Multi-Agent Actor-Critic (MAAC): MAAC is a recent algorithm that combines the advantages of policy-based and value-based methods. MAAC uses an actor-critic architecture to learn decentralized policies and value functions for each agent, while also incorporating a centralized critic to estimate the global value function.
Overall, the development of MARL has been driven by the need to address the challenges of coordinating multiple agents in complex environments. While there is still much to be learned in this field, recent advancements in deep learning and reinforcement learning have opened up new possibilities for developing more effective MARL algorithms.
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