actor-critic(AC)算法,是结合了policy based 和value based的方法,对二者进行了融合。 先看一下状态价值的表达,他有两部分组成: 1:动作概率密度函数 2:动作价值… 一文详解著名的 Actor-Critic、A2C 和 A3C 程序员眼罩 gzh:程序员眼罩,分享互联网技术和成长 ...
Multi-agent reinforcement learning (MARL) is essential for a wide range of high-dimensional scenarios and complicated tasks with multiple agents. Many attempts have been made for agents with prior domain knowledge and predefined structure. However, the interaction relationship between agents in a multi...
Multi-Agent Reinforcement Learning (MARL) is the discipline that focuses on models where agents dynamically learn policies through interaction with the environment. An agent’s goal is to maximize its local reward, a numerical-representation of a long-term objective [17]. In a MAS, multiple agent...
We observe that the A-CATs controller based on radial basis function networks (RBF (5)) outperforms others. This controller is benchmarked against controllers of discrete state Q-learning, Bayesian Q-learning, fixed time and actuated controllers; and the results reveal that it consistently ...
The Transformer-based Multi-Agent Actor-Critic Framework (T-MAAC) is based on MAPDN. Please refer to that repo for more documentation. Installation We suggest you install dependencies with Dockerfile and run the code with Docker. docker build . -t tmaac Downloading the Dataset We use load pr...
文章《Multi-UAV Cooperative Air Combat Decision-Making Based on Multi-Agent Double-Soft Actor-Critic》的引言部分详细阐述了多无人机(multi-UAV)协同空战决策问题的研究背景、现有技术的不足、研究动机、所提方法的概述以及文章的主要贡献。以下是对引言部分的进一步详细解读: ...
In this paper, a novel multi-robot social-aware efficient cooperative planner on the basis of off-policy multi-agent reinforcement learning (MARL) under partial dimension-varying observation and imperfect perception conditions is proposed. We adopt a temporal-spatial graph (TSG)-based social encoder ...
We call this approach Multi-Agent Graph-based soft Actor-Critic (MAGAC). We compare our proposed method with several classical MARL algorithms under the Multi-agent Particle Environment (MPE). The experimental results show that our method can achieve a faster learning speed w...
2024) or employing value decomposition (Wang et al., 2021a) to share the environment, these approaches adjust the signal control policy of each intersection from a network-wide perspective. Wang et al. (2021b) leveraged the cooperative vehicle infrastructure system to construct a MARL framework ...
MARL has been extensively studied in a limited state and action space [41], [42]. Independent Q-learning [43] uses an independent controller for each agent, which ignores the non-stationarity caused by the actions of other agents. Based on coordinate graphs, Guestrin et al. [12] proposed ...