本文主要提出了两种用于多机器人系统的安全多智能体强化学习(Safe Multi-Agent Reinforcement Learning, Safe MARL)算法:Multi-Agent Constrained Policy Optimization (MACPO)和MAPPO-Lagrangian。这两种算法旨在解决多机器人系统中的协作控制问题,确保各个智能体在最大化奖励的同时满足
This paper explores the combination of model-based methods and multi-agent reinforcement learning (MARL) for more efficient coordination among multiple agents. A decentralized model-based MARL method, Policy Optimization with Dynamic Dependence Modeling (POD2M), is proposed to dynamically determine the ...
为了降低整个学习过程中的样本复杂度,文中提出了一种 Adaptive Opponent-wise Rollout Policy Optimization (AORPO)算法。 Two Parts of Sample Complexity 论文的一个关键设定是多智能体之间是可通讯的,即在不知道对手策略的情况下,可以通过通讯协议知道对手在任何状态下采取的行动。 ego agent send a state to the...
Agents andmulti-agent systemshave been used to study and to simulate complex systems in different application domain where physical factor are present for energy minimizing, wherephysical objectstend to reach thelowest energy consumptionpossible within the physically constrained world. Furthermore, MAS hav...
This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction
A suite of test scenarios for multi-agent reinforcement learning. multiagent-reinforcement-learning UpdatedMar 11, 2025 Python A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient) multiagent-reinforcement-learningpytorch-rl ...
In this paper, we examine the role of these policy gradient and actor-critic algorithms in partially-observable multiagent environments. We show several candidate policy update rules and relate them to a foundation of regret minimization and multiagent learning techniques for the one-shot and ...
(COINE) Search, Optimization, Planning, and Scheduling (SOPS) Representation, Perception, and Reasoning (RPR) Engineering and Analysis of Multiagent Systems (EMAS) Modeling and Simulation of Societies (SIM) Human-Agent Interaction (HAI) Robotics and Control (ROBOT) Innovative Applications (IA) ...
A novel event-triggering mechanism which involves the neighbours’ information is derived for each agent to achieve a trade-off between resource usage and control performance. In such a framework, the DPC optimization problem is solved and information is exchanged only at triggering instants, thus ...
Multi-Step Policy Ensemble Optimization 本文进一步提出了一个简单的OPE算法,用于实现multi-step policy improvement。相较于之前的BPPO使用online的方式进行评估,本文更贴合offline RL的setting 更新方式如下:其中i代指ensemble中的第i个,而k代指迭代的次数(multi-step), r(\pi^i)=\frac{\pi^i(a|s)}{\pi^...