Sarsa 和Q-Learning 在强化学习中已经算是比较有名以及有广泛应用的算法了,这两个都是运用value-action function。Sarsa得名的原因是下图,而Q-learning是因为用了Q function所以得名,这两个算法非常相像,之后会具体说到这两者的区别。 Sarsa Algorithm for On-policy Control Convergence of Sarsa Sarsa converges to...
Letting computers have the ability of cognizing,expressing their emotions and training them to act human e- motions,is becoming hotspot of recent research.This paper,designs a model of emotion-automaton based on dynamic Q-learning arithmetic,in which defines an emotional unit.The emotional unit wil...
The first, named Fuzzy Q-learning, in an adaptation of Watkins' Q-learning for fuzzy Inference systems. The second, named Dynamical Fuzzy Q-learning, eliminates some drawbacks of both Q-learning and Fuzzy Q-learning. These algorithms are used to improve the rule based of a fuzzy controller....
intermittent Q-learningsuboptimal performanceZeno-freeThis paper proposes an intermittent model-free learning algorithm for linear time-invariant systems, where the control policy and transmission decisions are co-designed simultaneously while also being subjected to worst-case disturbances. The control policy...
Q-Learning and Dynamic Programming for World Grid Navigation. Reinforcement Learning experiment with insights into policy learning and hyper parameter tuning. - SamyuelDanyo/q-learning-dynamic-programming
与时间差分方法(如Q-Learning):动态规划通常在小规模问题上效果更好,因为它会考虑所有可能的状态转移。然而,在大规模或者未知环境中,Q-Learning等时间差分方法更实用。 与神经网络:动态规划可以和神经网络结合,用于处理更复杂、高维的状态空间。比如,深度Q网络(DQN)就是Q-Learning和神经网络的结合。
As the Q-learning algorithm always pursuits the maximum reward in long term,the number of pulse reversals,the value of CPS,and the change of the power outputs are introduced as the control variables in the reward function of the Q-learning controller. To get the maximum long-term reward,Q-...
This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-...
The experiments have also demonstrated that the QEEC approach is the most energy-efficient as compared to other task scheduling policies, which can be largely credited to the M/M/S queueing model and the Q-learning strategy implemented in QEEC. 展开 关键词: Cloud computing Task scheduling ...
Finally, the optimal weights are ensembled for the three sub‐predictors by the optimal weights generated using the Q‐learning algorithm, and the final results are obtained by combining their respective predictions. The results show that the forecasting capability of the proposed method outperforms ...