Q-learning作为一种经典的强化学习算法,其在离散状态下存在计算量高、收敛速度慢等问题。Speedy Q-learning是Q-learning的变种,目的是解决Q-learning算法收敛速度慢问题。为解决多智能体强化学习中“维数灾”问题,在Speedy Q-learnin...Q-learning作为一种经典的强化学习算法,其在离散状态下存在计算量高、收敛速度慢等问题。Speedy Q-learning是Q-learning的变种,目的是解决Q-learning算法收敛速度慢问题。为解决多智能体强化学习中“维数灾”问题,在Speedy Q-learning算法的基础上提出了一种基于动作采样的(action sampling based on Speedy Q-learning,ASSQ)算法。该算法采用集中训练-分散执行(centralized training with decentralized execution,CTDE)的框架,将上一迭代步更新后的Q值作为下一状态的最大Q值,有效降低了Q值的比较次数,整体上提升了算法的收敛速度。为减少学习阶段计算量,算法在集中训练阶段求取下一状态最大Q值时,并没有遍历所有联合动作Q值,而只在联合动作空间上进行部分采样。在动作选择和执行阶段,每个智能体又根据学习到的策略独立选择动作,从而有效提高了算法的学习效率。通过在目标运输任务上验证,ASSQ算法能够以100%的成功率学习到最优联合策略,且计算量明显少于Q-learning算法。展开更多
Ⅰ. The Development Plan for Auto-electronic technology in China 1. The Status Quo of China’s Auto-electronic Technology Application There is a clear disparity between China and developed countries in the field of au...Ⅰ. The Development Plan for Auto-electronic technology in China 1. The Status Quo of China’s Auto-electronic Technology Application There is a clear disparity between China and developed countries in the field of auto-electronic technology. The comprehensive level of China today’s auto-electronic展开更多
文摘Q-learning作为一种经典的强化学习算法,其在离散状态下存在计算量高、收敛速度慢等问题。Speedy Q-learning是Q-learning的变种,目的是解决Q-learning算法收敛速度慢问题。为解决多智能体强化学习中“维数灾”问题,在Speedy Q-learning算法的基础上提出了一种基于动作采样的(action sampling based on Speedy Q-learning,ASSQ)算法。该算法采用集中训练-分散执行(centralized training with decentralized execution,CTDE)的框架,将上一迭代步更新后的Q值作为下一状态的最大Q值,有效降低了Q值的比较次数,整体上提升了算法的收敛速度。为减少学习阶段计算量,算法在集中训练阶段求取下一状态最大Q值时,并没有遍历所有联合动作Q值,而只在联合动作空间上进行部分采样。在动作选择和执行阶段,每个智能体又根据学习到的策略独立选择动作,从而有效提高了算法的学习效率。通过在目标运输任务上验证,ASSQ算法能够以100%的成功率学习到最优联合策略,且计算量明显少于Q-learning算法。
文摘Ⅰ. The Development Plan for Auto-electronic technology in China 1. The Status Quo of China’s Auto-electronic Technology Application There is a clear disparity between China and developed countries in the field of auto-electronic technology. The comprehensive level of China today’s auto-electronic
