The learning Reinforcement Project (LRP) was developed for the purpose of reinforcing the academic and social competencies of all students in English language classes, so that they would be better prepared for more accurate and fluent communication with others as well as a happy social life in a democratic society. This report presents the results of a 2-year study employing cooperation and reflection, as a means to reinforce learning in language teaching. It discusses how it was used and outlines a. plan for its implementation and integration into classrooms. The LRP was a time-limited intervention designed to prepare students fully to continue achieving. By providing 110-220 hours of additional instruction over two academic years, the LRP has been successful in helping students to make both statistically and practically significant achievement and aptitude gains. To document the direct effect of the program and eliminate alternative hypotheses for assessed gains, a pre-post test, comparison group design was used. The program, using the same research design, was implemented in three different school systems with similar results.    

In recent years, researches on Reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network Reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applications. Although many surveys investigated general RL, no survey is specifically dedicated to the combination of artificial neural networks and RL. This paper therefore describes the state of the art of NNRL algorithms, with a focus on robotics applications. In this paper, a comprehensive survey is started with a discussion on the concepts of RL. Then, a review of several different NNRL algorithms is presented. Afterwards, the performances of different NNRL algorithms are evaluated and compared in learning prediction and learning control tasks from an empirical aspect and the paper concludes with a discussion on open issues.

Using Information Technology techniques have been increased complication and dynamicity of supply-and-demand systems like auctions. In this paper, we introduce a novel method by applying Reinforcement learning (RL) price offer as one of the robust methods of agent learning which can be used in interactive conditions with minimum level of information in auction and reverse auction. Negotiation as one of the challengeable and complicated behaviors is caused an agreement on price in auctions. The main aim of our method is maximizing seller’s and customer’s profits. We formulate seller and customer selection in form of two different RL problems. All of the RL parameters like states, actions, and Reinforcement function are defined. Also, we describe an experimental method to compare with our proposed method for proving advantages of our method.

Web application (app) exploration is a crucial part of various analysis and testing techniques. However, the current methods are not able to properly explore the state space of web apps. As a result, techniques must be developed to guide the exploration in order to get acceptable functionality coverage for web apps. Reinforcement learning (RL) is a machine learning method in which the best way to do a task is learned through trial and error, with the help of positive or negative rewards, instead of direct supervision. Deep RL is a recent expansion of RL that makes use of neural networks’ learning capabilities. This feature makes Deep RL suitable for exploring the complex state space of web apps. However, current methods provide fundamental RL. In this research, we offer DeepEx, a Deep RL-based exploration strategy for systematically exploring web apps. Empirically evaluated on seven open-source web apps, DeepEx demonstrated a 17% improvement in code coverage and a 16% enhancement in navigational diversity over the stateof-the-art RL-based method. Additionally, it showed a 19% increase in structural diversity. These results confirm the superiority of Deep RL over traditional RL methods in web app exploration.

Web application  rewalls (WAFs) are used for protecting web applications from attacks such as SQL injection, cross-site request forgery, and cross-site scripting. As a result of the growing complexity of web attacks, WAFs need to be tested and updated on a regular basis. There are various tools and techniques to verify the correct performance of a WAF. But most of the techniques are manual or use brute-force attacks, so su er from poor e cacy. In this work, we propose a solution based on Reinforcement learning (RL) to discover malicious payloads, which are able to bypass WAFs. We provide an RL framework with an environment compatible with OpenAI gym toolset standards. The environment is employed for training agents to implement WAF circumvention tasks. The agent mutates the syntax of a malicious payload using a set of modi cation operators as actions, without changes to its semantic. Then, upon WAF's reaction to the payload, the environment ascertains a reward for the agent. Eventually, based on these rewards, the agent learns a suitable sequence of mutations for any malicious payload. The payloads, which bypass the WAF determine rules defects, which can be further used in rule tuning for rule-based WAFs. Also, it can enrich the machine learning-based WAFs datasets for retraining. We use Q-learning, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) algorithms with the deep neural network. Our solution is successful in evading signature-based and machine learning-based WAFs. While our focus in this work is on SQL injection, the method can be simply extended to use for any string-based injection attacks.

Introduction In developed societies, residential customers use high-level appliances. The progress in the smart grids and the internet of things have eased the way for home energy management to schedule controllable appliances. Looking to demand increment, demand response strategies aiming at energy management, to achieve goals such as demand reduction and improving reliability, has received attention. A deep review of the existing literature shows the notable efforts put into optimizing the home energy management problem through classic and meta-heuristic optimization algorithms such as game theory, genetic algorithm, and PSO. But, it is worth saying that these algorithms are not pragmatic due to the inherent nature of the home energy management problem. To be more precise, as the environment of the problem changes continuously, these algorithms fail to solve the problem. Hence, some essential assumptions such as considering fixed scenarios are presumed in previous works to enable the conventional algorithm to solve the problem. This is while machine learning addresses this issue by extracting the main features from input data and constructing a general description of the environment. Implementation of machine learning-based algorithms to a home energy management problem requires smart appliances. Hence, in the case of having a smart home, taking the advantage of artificial intelligence for energy management would be feasible and useful. It should be noted that electricity cost reduction can make the demand response program inviting, where customer satisfaction is taken into consideration. Accordingly, customer satisfaction should be considered in the problem formulation. Regarding the mentioned issues, lately, with the remarkable progress in machine learning, novel algorithms evolved for solving optimal decision-making problems such as demand response. Machine learning can be categorized into three main categories, namely supervised learning, unsupervised learning, and Reinforcement learning (RL). Among them, Reinforcement learning has shown notable performance in decision-making problems. Q-learning is a model-free RL algorithm that solves nonlinear problems through estimating and maximizing the cumulative reward, triggered by decided actions. The fundamental idea of this algorithm is to identify the best action in each situation. This paper aims to provide a day-ahead demand response program for a smart home. It is done by specifying the quantity of the energy consumption of each appliance, aiming to reduce the electricity cost and user dissatisfaction. In this respect, it is presumed that the smart home is equipped with smart appliances. Moreover, smart meters are installed on appliances to monitor the statuses and receive the command signals from the devices at each hour. These appliances can be divided into three categories, non-responsive, time-shiftable, and controllable loads. Dishwasher and washing machine as time-shiftable loads, EV, air conditioner, and lighting system as controllable loads, and TV and refrigerator as non-responsive loads are taken into account. All in all, we recommend an advanced home energy management system proposing the following contributions: i) Proposing a day-ahead multi-agent Q-learning method to minimize the electricity cost. ii) Proposing a satisfaction-based framework, which employs a precise model of the customer dissatisfaction functions (i. e., thermal comfort, battery degradation, and desirable operation period). Materials and methods In this paper, a multi-agent Q-learning approach is used to solve the home energy management for a smart home. Q-learning is a popular model-free algorithm among Reinforcement learning algorithms, due to the fact that its convergence is proven, and it is feasible to implement, as well. In order to deploy Q-learning on a home energy management system, first of all, smart home should be formed as a Markov decision process. A Markov decision process consists of four fundamental parameters namely, state, action, reward, and transition probability matrix. Afterward, an agent is trained through experiencing a specific state, taking an action, transition to a new state, and calculating the cumulative reward. By doing so, after visiting a considerable number of states and taking diverse decisions, it will learn gradually to select the optimum action whatever the state is. Another fundamental aspect of this paper is the proposed approach to take customer satisfaction into account. In this paper, a non-linear thermal comfort model, non-linear desirable operation period model, and linear battery degradation model are deployed to consider the customer dissatisfaction, precisely. It should be noted that all simulations have been implemented by python 3. 6 programming language without making use of any commercial solver. Result Various case studies have been designed to verify the effectiveness of the proposed method. Scenario 1 is designed to simulate the behavior of a smart home associated with a random manner of energy usage. Scenario 2 is designed to verify the effectiveness of the proposed home energy management system, where Q-learning is conducted. In this case, battery degradation is overlooked. Scenario 3 is similar to the previous one, where battery degradation is also taken into consideration. Comparing the obtained results indicates that the proposed algorithm has successfully reduced the electricity bill by 31. 3% and 24. 8% in scenarios 2 and 3, respectively. It is worth saying that customer satisfaction is not violated in mentioned scenarios. Furthermore, in order to evaluate the effect of thermal comfort on the electricity bill, another case study is deployed, where the thermal comfort coefficient is decreased to smaller magnitudes. As expected, the less thermal comfort coefficient, the less electricity bill. The reason behind this is that having a lower thermal comfort coefficient leads to less importance of temperature control compared to the electricity bill. Conclusion This paper proposed a method for home energy management, regarding minimizing the electricity bill and user discomfort. In this paper, a multi-agent Reinforcement learning via Q-learning is used to make optimal decisions for home appliances, which are categorized into non-shiftable loads, time-shiftable loads, and controllable loads. Comparing to classic optimization methods, the proposed approach in this paper is capable of modeling more appliances and solving complex problems, due to the inherent nature of the Q-learning algorithm. Implementing the proposed method in the numerical study section led to a 24. 8% electricity bill reduction. The numerical results prove the effectiveness of the proposed approach.