基于蚁群算法和轮盘算法的多Agent Q学习

提出了一种新颖的基于Q-学习、蚁群算法和轮盘赌算法的多Agent强化学习。在强化学习算法中，当Agent数量增加到足够大时，就会出现动作空间灾难性问题，即：其学习速度骤然下降。另外，Agent是利用Q值来选择下一步动作的，因此，在学习早期，动作的选择严重束缚于高Q值。把蚁群算法、轮盘赌算法和强化学习三者结合起来，期望解决上述提出的问题。最后，对新算法的理论分析和实验结果都证明了改进的Q学习是可行的，并且可以有效地提高学习效率。

Multiagent Q-learning based on ant colony algorithm and roulette algorithm

Authors present a novel Multiagent Reinforcement Learning Algorithm based on Q-Learning,ant colony algorithm and roulette algorithm.In reinforcement learning algorithm,when the number of agents is large enough,all of the action selection methods will be failed:the speed of learning is decreased sharply.Besides,as the Agent makes use of the Q value to choose the next action,the next action is restrainted seriously by the high Q value,in the prophase.So,authors combine the ant conlony algorithm,roulette algor...