The decision of the handovers is to ensure that if there is any requirement of the vertical handovers using dynamic Q-learning algorithms in which entropy function is used to predict the threshold according to the characteristics of the environment. The network selection process is done using ...
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....
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-...
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 ...
learning algorithm to this regularized mean-field game using fitted Q-learning. The regularization term in general makes reinforcement learning algorithm more robust to the system components. Moreover, it enables us to establish error analysis of the learning algorithm without imposing restrictive ...
Previously, the Concurrent Q-Learning algorithm was developed, based on Watkin's Q-learning, which learns the relative proximity of all states simultaneously. This learning is completely independent of the reward experienced at those states and, through a simple action selection strategy, may be ...
Policy iteration:以greedy为例 Find optim policy: \[ v_{\pi}(s)=\max _{a \in \mathcal{A}} q_{\pi}(s, a) \]主动改变策略,策略改变之后进行评估 根据q值,从集合A中选a,更新策略\(\pi\),使新q大于之前一步 \[ q_{\pi}\left(s, \pi^{\prime}(s)\right)=\max _{a \in \math...
CUDA_VISIBLE_DEVICES=0,1 OMP_NUM_THREADS=4 python -m torch.distributed.launch --master_port 12345 --nproc_per_node=2 train.py --id s0-QDMN --stage 0 To train on DAVIS and YouTube, use this command: CUDA_VISIBLE_DEVICES=[GPU_ids] OMP_NUM_THREADS=4 python -m torch.distributed.laun...