Specifically, we first adopt the bootstrapped Deep Q-Network (DQN) algorithm to induce exploration via an ensemble of behavior policies, and it outperforms the vanilla DQN in both efficiency and robustness on a handcrafted asymmetric isolated intersection. Further, we develop a multi-agent DQN ...
Multi-agent deep Q-networks (MADQNs) target to enforce a self-learning softwarization, optimize resource allocation policies, and advocate computation offloading decisions. With gathered network conditions and resource states, the proposed agent aims to explore various actions for estimating expected long...
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...
The multi-agent path planning problem presents significant challenges in dynamic environments, primarily due to the ever-changing positions of obstacles an
Agent Networks:每个智能体都有一个深度Q网络(DQN)或循环神经网络(RNN),用于估计该智能体的局部Q值。 Mixing Network:这个网络负责将所有智能体的Q值以单调的方式混合成全局Q值。混合网络的权重由另一个称为超网络的网络动态生成,这些权重是非负的,确保了Q值的单调组合。
Multi-agent Deep Q-Network (MADQN) [40]: This is the multi-agent version of Deep Q-Network (DQN) [25], which is an off-policy RL method by applying a deep neural network to approximate the value function and an experience replay buffer to break the correlations between samples to stab...
To address this challenge, a multi-agent deep reinforcement learning framework was proposed to optimize the energy management of the building. In this paper, a dueling double deep Q-network was used for optimization of single agent, and value-decomposition network was put forward to solve the ...
This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global $\alpha$-fairness objective. In the conventional DRL framework, feedback/reward given to the agent is always correctly...
Since the Deep Q-Network was proposed, multi-agent reinforcement learning (MARL) has been a focused piece of research in solving FJSP. To map FJSP to the MARL-solving architecture, each job or robot is regarded as an agent in the scheduling environment, where multiple agents cooperate and ...
multiagent 是指同时有多个 agent 更新 value 和 Q 函数,主要的算法有:q learning, friend and foe q leaning,correlated q learning,在每个训练步骤,学习器会考虑多个 agent 的联合 states,actions,reward,来更新 q 值,其中会用到函数 f 选择价值函数。 下图是单一 agent 和 多个 agent 的对比图,可以很直观...