一种新的基于值函数迁移的快速Sarsa算法

摘要

知识迁移是当前机器学习领域的一个新的研究热点。其基本思想是通过将经验知识从历史任务到目标任务的迁移，达到提高算法收敛速度和收敛精度的目的。针对当前强化学习领域中经典算法收敛速度慢的问题，提出在学习过程中通过迁移值函数信息，减少算法收敛所需要的样本数量，加快算法的收敛速度。基于强化学习中经典的在策略Sarsa算法的学习框架，结合值函数迁移方法，优化算法初始值函数的设置，提出一种新的基于值函数迁移的快速Sarsa算法———VFT-Sarsa 。该算法在执行前期，通过引入自模拟度量方法，在状态空间以及动作空间一致的情况下，对目标任务中的状态与历史任务中的状态之间的距离进行度量，对其中相似并满足一定条件的状态进行值函数迁移，而后再通过学习算法进行学习。将VTF-Sarsa算法用于Random Walk问题，并与经典的Sarsa算法、Q学习算法以及具有较好收敛速度的QV算法进行比较，实验结果表明，该算法在保证收敛精度的基础上，具有更快的收敛速度。%Knowledge Transfer has gradually became a research hot pot in machine learning ,which tries to transfer the knowledge from the historical tasks to the target task in order to speed up the convergence rate and improve the performance of al-gorithms .With respect to the slow convergence rate of traditional reinforcement learning algorithms ,this paper proposed to transfer the value function between different similar learning tasks with the same state space and action space ,which tries to reduce the need-ed samples in the target task and speed up the convergence rate .Based on the framework of on-policy Sarsa algorithm ,combined with the value function transfer method ,this paper put forward a novel fast Sarsa algorithm based on the value function transfer—VFT-Sarsa .At the beginning ,the algorithm uses Bisimulation metric to measure the distance between states in target task and histor-ical task on the condition that these tasks have the same state space and action space ,transfers the value function if the distance meets some condition ,and finally executes the learning algorithm .At the end ,apply the proposed algorithm in Random Walk ,com-pared with Sarsa algorithm ,Q-Learning and QV algorithm ,the results show that the proposed algorithm can get a better convergence rate with a good performance .