【摘要】 提出了一种固定长度经验回放的思想,并将该思想与一步Q和Peng Q(λ)学习算法相结合,得到了相应的改进算法。该文采用不同的回放长度L将改进的算法应用在网格环境和汽车爬坡问题中进行了仿真。结果表明,改进的一步Q学习算法在两个例子中都比原算法具有更好的学习效率。改进的Peng Q(λ)学习在马尔可夫环境中对选择探索动作非常敏感,增大L几乎不能提高学习的效率,甚至会使学习效率变差;但是在具有非马尔可夫属性的环境中对选择探索动作比较不敏感,增大L能够显著提高算法的学习速度。实验结果对如何选择适当的L有着指导作用。更多还原

【Abstract】 In order to improve the learning efficiency of Q-learning,an idea of experience replay with fixed history length is proposed.This idea is integrated into one-step Q and Peng Q(λ)-learning respectively.The improved algorithms are investigated with different history length L in two learning tasks: grid world and mountain car problem.Empirical results show that improved one-step Q-learning has better efficiency than original one-step Q in both tasks.The improved Peng Q(λ) is quite sensitive to exploratory actions in Markovian environment.Increasing L can hardly enhance the performance of the algorithm,and the performance may deteriorate.However,improved Peng Q(λ) is less sensitive to exploratory actions in non-Markovian environments,and increasing L monotonously speeds up policy learning.The experimental findings also provide guidance to appropriate history length L更多还原