Function approximators, such as deep neural networks, have successfully been used in single-agent environments with high dimensional state-spaces. We propose the Multi-agent Double Deep Q-Networks algorithm, an extension of Deep Q-Networks to the multi-agent paradigm. Two common techniques of multi...
该论文主要将Lenient应用于在ERM中,并将这一理念扩展到MADRL,并且证明了并行学习隐式协调策略的Lenient-MADRL的agent能够在随机的困难协调任务中收敛于最优的合作策略。 由于目标移动的问题,在单个agent中以往的强化学习算法不适合在多个智能体合作系统中应用。而hysteretic Q-learning 和 leniency算法成功地应用到了MAD...
reinforcement-learningdeep-reinforcement-learningpytorchmulti-agentdqnrldeep-q-networkddpgdrlactor-criticdeep-deterministic-policy-gradientproximal-policy-optimizationppoadvantage-actor-critica2cacktrmadrl UpdatedNov 11, 2017 Python 🐝 GPTSwarm: LLM agents as (Optimizable) Graphs ...
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...
Multiagent systems appear in most social, economical, and political situations. In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic...
Multi-Agent Deep Reinforcement Learning for Resource Allocation in the Multi-Objective HetNet Resource allocation in a heterogeneous network is an NP-hard problem, especially in 5G network scenarios. Multiobjective optimization in resource allocatio... H Nie,S Li,Y Liu - International Wireless Communi...
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V Communicationarxiv. This repo contains the source code of the toy example that we used in our paper to test the performance of the algorithm. Abstract
Deep Q-learning (DQN) for Multi-agent Reinforcement Learning (RL) DQN implementation for two multi-agent environments: agents_landmarks and predators_prey (See details.pdf for a detailed description of these environments). Code structure ./environments/: folder where the two environments (agents_...
也是基于此本文提出了两种算法:IA2C, MA2C,IA2C不考虑多智能之间的信息交互,每一个agent训练自己的policy和value -network,基于全局奖励和状态在智能体之间共享的假设,定义对应的local_reward,policy_loss, Value_loss MA2C考虑智能体之间的信息交互,有两个主要改进: ...
The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its singl