基于效用的Agent多动作选择承诺机制
Decision making of an agent depends on the other agents' behavior while sharing information is not always possible. On the other hand, predicting other agents' policies while they are also learning is a difficult task. Also, some agents in a multi-agent environment may not behave rationally. ...
这里s表示状态,ai表示agent i的动作,Qi表示agent i的动作价值函数,a1:i−1表示前序agent 1到i-1选择的动作序列。 ②反向依赖(Backward Dependency):更新一个agent的动作Q值时要依赖于后续agent对之前动作的反应。即更新agent i的动作Q值的target依赖于agent i+1到n对前序动作a1:i的最优反应: yi=r+γmaxai...
Macro-Action-Based Deep Multi-AgentReinforcement LearningYuchen Xiao Joshua Hoffman Christopher AmatoKhoury College of Computer SciencesNortheastern University, United States{xiao.yuch, hoffman.j}@husky.neu.edu, c.amato@northeastern.neuAbstract: In real-world multi-robot systems, performing high-quality,...
Explainable Action Advising for Multi-Agent Reinforcement Learning 。 http://t.cn/A6NXbJsp
In the FJSP environment, the reinforcement agent needs to schedule an operation belonging to a job on an eligible machine among a set of compatible machines at each timestep. This means that an agent needs to control multiple actions simultaneously. Such a problem with multi-actions is ...
Multi-Agent Plan Recognition (MAPR) aims to recognize dynamic team structures and team behaviors from the observed team traces (activity sequences) of a set of intelligent agents. Previous MAPR approaches required a library of team activity sequences (team plans) be given as input. However, co...
Finally, we prove that multiagent Q-learning values converge to optimal values. Simulation results are reported to illustrate the validity of the proposed multiagent Q-learning algorithm. 展开 关键词: Markov processes learning (artificial intelligence multi-agent systems MARP Markov model cooperative ...
Undesired state-action prediction in multi-agent reinforcement learning for linked multi-component robotic system control Fernandez-Gauna, B., Marques, I., Graña, M.: Undesired state-action prediction in multi-agent reinforcement learning. application to multicomponent ......
其中第一个式子意为每一个 agent 要最大化他的个人利益,第二个式子表示为了实现 safe RL,每一步的 joint action 需要满足一些 constraints,注意到这里是 joint action 需要满足一些条件,这说明在安全性保证方面,不同 agent 的 action 是要有联动的。在实际算法中,我们通过 constrained optimization 来选择 action。