فهرست مطالب niloofar alavi

Reinforcement learning (RL) refers to the training of machine learning models to make a sequence of decisions on which an agent learns by interacting with its environment, observing the results of interactions and receiving a positive or negative reward, accordingly. RL has many applications for multi-agent systems, especially in dynamic and unknown environments. However, most multi-agent reinforcement learning (MARL) algorithms suffer from some problems specifically the exponential computational complexity to calculate the joint state-action space, which leads to the lack of scalability of algorithms in realistic multi-agent problems. Applications of MARL can be categorized from robot soccer, networks, cloud computing, job scheduling, and to optimal reactive power dispatch. In the area of reinforcement learning algorithms, there are serious challenges such as the lack of application of equilibrium-based algorithms in practice and high computational complexity to find equilibrium.  On the other hand, since agents have no concept of equilibrium policies, they tend to act aggressively toward their goals, which it results the high probability of collisions. Consequently, in this paper, a novel algorithm called Regularized Knowledge Transfer for Multi-Agent Reinforcement Learning (RKT-MARL) is presented that relies on Markov decision process (MDP) model. RKT-MARL unlike the traditional reinforcement learning methods exploits the sparse interactions and knowledge transfer to achieve an equilibrium across agents. Moreover, RKT-MARL benefits from negotiation to find the equilibrium set. RKT-MARL uses the minimum variance method to select the best action in the equilibrium set, and transfers the knowledge of state-action values across various agents. Also, RKT-MARL initializes the Q-values in coordinate states as coefficients of current environmental information and previous knowledge. In order to evaluate the performance of our proposed method, groups of experiments are conducted on five grid world games and the results show the fast convergence and high scalability of RKT-MARL. Therefore, the fast convergence of our proposed method indicates that the agents quickly solve the problem of reinforcement learning and approach to their goal.

American Orthopedic Foot and Ankle Society Score (AOFAS) is a reliable and reproducible measurement tool which is commonly used for the assessment of foot and ankle conditions. In this study we aimed to translate and assess the psychometric properties of the Persian version of AOFAS questionnaire.
In this study, we enrolled 53 patients with ankle and hindfoot conditions. Our study was conducted according to five staged cross-cultural adaption steps including translation, synthesis, back translation, expert committee review, and pretesting. After that reliability of the subjective parts calculated by Cronbach’s alpha and the intraclass correlation coefficient (ICC) and the reliability of the objective items estimated using Cohen’s kappa test. Also, construct validity was assessed by testing the Persian AOFAS against the SF-36 questionnaire.
Chronbach’s alpha coefficient was 0.696, which was considered acceptable. Furthermore, the test-retest reliability measured by using the ICC for the subjective subscales was 0.853 (P In our study, Persian translation of AOFAS demonstrated acceptable validity and reliability with no need to be culturally adapted.