multiagent-reinforcement-learningmulti-agent-learning UpdatedOct 17, 2024 opendilab/DI-engine Star3.2k OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P. pythonreinforcement-learningimpalareinforcement-learning-algorithmsminigridatariimitation-learningdistributed-systemdrlinverse...
Multi-Agent Reinforcement Learning in JAX 🎉Update: JaxMARL was accepted at NeurIPS 2024 on Datasets and Benchmarks Track. See you in Vacouver! JaxMARL combines ease-of-use with GPU-enabled efficiency, and supports a wide range of commonly used MARL environments as well as popular baseline ...
多智能体强化学习:基础与现代方法(Multi-Agent Reinforcement Learning: Foundations and Modern Approaches) 2023年5月29日,来自爱丁堡大学信息学院的Stefano V. Albrecht副教授发布了多智能体强化学习领域的书籍。 2024年12月10日发布预印版 一、作者简介 作为英国皇家学会行业研究员,他与Five AI/Bosch的一个团队合作...
原文: Actor-Attention-Critic for Multi-Agent Reinforcement Learning 作者: Shariq Iqbal, Fei Sha 论文发表时间: 2019年 代码: github.com/shariqiqbal2 多agent环境: github.com/openai/multi 1. 多agent环境中有效学习,前人一共提出2种方法,方法1单独的训练每个agent,其它agent作为环境的一部分,所以很难学习...
D: display agent info (at every time step) ESC: exit Please Cite The paper that first uses this implementation of Level-based Foraging (LBF) and achieves state-of-the-art results: @inproceedings{christianos2020shared, title={Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning}...
More recently, machine learning has been contributing to this endeavour with promising results. However, all efforts have focused on supervised learning, which is difficult to generalize beyond training data. Here we introduce multi-agent reinforcement learning as an automated discovery tool of ...
Xing-Xing, L., Yang-He, F., Yang, M., Guang-Quan, C., Jin-Cai, H., Qi, W., Yu-Zhen, Z., & Zhong, L. (2020). Deep multi-agent reinforcement learning: A survey.Acta Automatica Sinica,46(12), 2537–2557. MATHGoogle Scholar ...
Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing multi-agent RL methods typically scale poorly in the problem size. Therefore, a key challenge is to translate the success...
This paper proposes an approach for learning to coor- dinate verbal and non-verbal behaviours in interactive robots. It is based on a hierarchy of multiagent reinforcement learners executing verbal and non-verbal actions in parallel. Our approach is evaluated in a conversational humanoid robot that...
Mean Field Multi-Agent Reinforcement Learning. Contribute to mlii/mfrl development by creating an account on GitHub.