Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall

A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation.In this paper,a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task.Firstly,the physical human-robot cooperation model,including the role factor is built.Then,a reinforcement learningmodel that can adjust the role factor in real time is established,and a reward and actionmodel is designed.The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s Jerk during the repeated installation.Finally,the roles adjustment rule established above continuously improves the comprehensive performance.Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified.The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s Jerk.

The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities

In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching.

Application of Cooperative Learning and Flipped Classrooms in University Basketball Courses in China

This study investigates the application of the teaching model combining cooperative learning and flipped classrooms in university basketball courses in China.By analyzing the advantages and disadvantages of the traditional basketball teaching model and students’satisfaction with the course,the necessity of implementing cooperative learning and flipped classrooms is proposed.The study planned in detail the implementation strategies before class,in the classroom,and after class,and compared them with the control group through an experimental design.The experimental results showed that the new teaching mode demonstrated significant advantages in terms of learning outcomes,student satisfaction,and teacher evaluation.This study provides a valuable reference for the future reform of the physical education curriculum.

Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning

To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.

Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

The Effects of Cooperative-learning in the Oral English Class

Oral English teaching is one of the essential parts in language teaching. However, the oral English teaching in Chinese college English large-scale classes is far from satisfactory. In the instruction process arise a variety of problems such as the passiveness of learners, the failure of timely feedback transmission and fewer chances of oral English practice.This study aims to probe into the effects of cooperative-learning in the oral English teaching of large-scale classes. It is the author's belief that cooperative learning may effectively improve college oral English teaching in large-scale classes.

Research on Compatible Mode of Self-directed Learning and Cooperative Learning in Blended Teaching of Basic English Course

In the information age,blended teaching,no matter online or offline,has become the mainstream of college teaching reform.In this teaching model,self-directed learning and cooperative learning are the two main learning approaches.On the online teaching platform,students mainly learn knowledge-based content by self-directed learning,while practising their language skills by cooperative learning in flipped classroom activities.On one hand,it advocates student-centered strategy so as to improve students autonomous learning ability;on the other hand,teachers serve as a guide to organize the classroom activities;meanwhile,they give timely feedback to students in order to promote students’learning ability.In blended teaching model,this mutually compatible and reinforcing model of self-directed learning and cooperative learning is undoubtedly helpful to improve the teaching efficiency.

The Effect of Cooperative Learning in Teaching EFL Reading for Chinese Non-English Majors

In order to examine the effect of cooperative learning in teaching reading for Chinese non-English majors,an experiment was done on cooperative learning in reading course.After a four-month experiment,a conclusion was drawn that there really existed significant difference in the English reading proficiency between students who received cooperative learning instruction and those who adopted non-cooperative learning instruction.This paper concludes that according to the statistics,cooperative learning is an efficient way to improve students' reading proficiency.

UAV cooperative air combat maneuver decision based on multi-agent reinforcement learning

In order to improve the autonomous ability of unmanned aerial vehicles(UAV)to implement air combat mission,many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out,but these studies are often aimed at individual decision-making in 1 v1 scenarios which rarely happen in actual air combat.Based on the research of the 1 v1 autonomous air combat maneuver decision,this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning.Firstly,a bidirectional recurrent neural network(BRNN)is used to achieve communication between UAV individuals,and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established.Secondly,through combining with target allocation and air combat situation assessment,the tactical goal of the formation is merged with the reinforcement learning goal of every UAV,and a cooperative tactical maneuver policy is generated.The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning,the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.

A Study on English Reading Teaching With Cooperative Learning By Means of Task-Based Teaching in Higher Vocational Education

Higher Vocational Education(HVE) is an important component of higher education in our country.The English Curriculum Standard of HVE considers promoting the students' reading comprehension as one of the most important goals in English teaching.Task-based CL emphasizes the importance of education as a vehicle for teaching citizens to live cooperatively in a social democracy and teachers are not the only sources of knowledge and students should be able to help each other and learn in an interdependent way.The heart of cooperative learning is student-centered.Therefore,task-based CL can overcome the shortcomings in traditional teaching models,and the goal of improving reading comprehension can be achieved for the teachers of reading by realizing the mental process of reading comprehension and how prior knowledge can enhance a reader's interaction with the text through CL.This thesis consists of five parts.Part one,two and three briefly introduces the background and significance of the study,definitions and models of cooperative learning and reading comprehension.Part four and five are the key sections of the thesis,which introduces the hypotheses,subjects,instrument,procedure of the study and the analysis of the results of the study.Part five draws the conclusion of the study.

reducing pre-school education norma 1 school students' affective filter in english learning though cooperative learning

during the past twenty year,many of the major developments in language teaching in some way related to the need to acknowledge affective factors in language learning.

Cooperative Multi-Agent Reinforcement Learning with Constraint-Reduced DCOP

Cooperative multi-agent reinforcement learning（ MARL） is an important topic in the field of artificial intelligence,in which distributed constraint optimization（ DCOP） algorithms have been widely used to coordinate the actions of multiple agents. However,dense communication among agents affects the practicability of DCOP algorithms. In this paper,we propose a novel DCOP algorithm dealing with the previous DCOP algorithms＇ communication problem by reducing constraints.The contributions of this paper are primarily threefold：（1） It is proved that removing constraints can effectively reduce the communication burden of DCOP algorithms.（2） An criterion is provided to identify insignificant constraints whose elimination doesn＇t have a great impact on the performance of the whole system.（3） A constraint-reduced DCOP algorithm is proposed by adopting a variant of spectral clustering algorithm to detect and eliminate the insignificant constraints. Our algorithm reduces the communication burdern of the benchmark DCOP algorithm while keeping its overall performance unaffected. The performance of constraint-reduced DCOP algorithm is evaluated on four configurations of cooperative sensor networks. The effectiveness of communication reduction is also verified by comparisons between the constraint-reduced DCOP and the benchmark DCOP.

The Effects of Cooperative Learning in Enhancing the Performance Level of Grade-10 Mathematics Students in Talavera National High School in the Philippines

Mathematics is considered by many students as a difficult academic subject. To improve their performance, various learning strategies are employed by teachers abandoning the conventional method. This study focused on the cooperative learning as a method to enhance level of performance of grade 10 mathematics students in the Philippines. It looked into the method and how it influenced students’ performance. By using a quasi-experimental research method, the following results are generated: 1) the method positively improved the performance in mathematics of the control and experimental groups;2) while effectiveness assessment showed a highly effective result;and 3) the application of the method showed a positive impact on the participants as shown by their positive perception of the subject mathematics.

Distributed Cooperative Learning for Discrete-Time Strict-Feedback Multi Agent Systems Over Directed Graphs

This paper focuses on the distributed cooperative learning(DCL)problem for a class of discrete-time strict-feedback multi-agent systems under directed graphs.Compared with the previous DCL works based on undirected graphs,two main challenges lie in that the Laplacian matrix of directed graphs is nonsymmetric,and the derived weight error systems exist n-step delays.Two novel lemmas are developed in this paper to show the exponential convergence for two kinds of linear time-varying(LTV)systems with different phenomena including the nonsymmetric Laplacian matrix and time delays.Subsequently,an adaptive neural network(NN)control scheme is proposed by establishing a directed communication graph along with n-step delays weight updating law.Then,by using two novel lemmas on the extended exponential convergence of LTV systems,estimated NN weights of all agents are verified to exponentially converge to small neighbourhoods of their common optimal values if directed communication graphs are strongly connected and balanced.The stored NN weights are reused to structure learning controllers for the improved control performance of similar control tasks by the“mod”function and proper time series.A simulation comparison is shown to demonstrate the validity of the proposed DCL method.

Action Research on Application of Cooperative Learning in Business English-Viewing, Listening ＆ Speaking

Business English-Viewing, Listening ＆ Speaking is a practical course for business English majors. Cooperative learning is the teaching mode mainly based on the group work. The author applied the cooperative learning in the teaching for 10 weeks. The action research attempts to investigate the feasibility and effectiveness of cooperative learning through teaching practice, questionnaire, interview, and classroom observation. The results show that cooperative learning can improve the learning behavior, and facilitate the students＇ communicating skills

The Application of Cooperative Learning in College English Teaching

Cooperative learning has developed into a creative and effective teaching theory and strategy. Cooperative learning can not only motivate students' initiative and enthusiasm in learning, but promote the interaction between the teacher and students.This paper analyzes the connotations and benefits of cooperative learning, explores reasons for existing problems in the application of cooperative learning in college English teaching and presents strategies for effective implementation of cooperative learning.

Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.

A review of mobile robot motion planning methods:from classical motion planning workflows to reinforcement learning-based architectures

Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is challenged.With the development of machine learning,the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature.The DRL-based motion planner is model-free and does not rely on the prior structured map.Most importantly,the DRL-based motion planner achieves the unification of the global planner and the local planner.In this paper,we provide a systematic review of various motion planning methods.Firstly,we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features.Then,we concentrate on summarizing reinforcement learning(RL)-based motion planning approaches,including motion planners combined with RL improvements,map-free RL-based motion planners,and multi-robot cooperative planning methods.Finally,we analyze the urgent challenges faced by these mainstream RLbased motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research.

Research into the Application of Cooperative Learning to English Reading Teaching of Art Majors in Independent Colleges

This paper first stated some related theories of cooperative learning and English reading teaching.And then an applicationof cooperative learning in English reading teaching of art majors in independent college is implemented based on these literature re-views.The aim of the experiment is to explore the impact of the application of cooperative learning strategies.It mainly discussedthe following questions:(1)Can the group cooperative learning improve students'reading achievement?(2)What factors contributeto the success/failure of cooperative learning?(3)What are the favorite cooperative learning models in reading classes?The experi-ment proved that cooperative learning does not truly improve art students’reading achievement in independent college,but it real-ly has a positive role in promoting the students'learning interest,motivation and emotional attitude.The experiment also indicatesthat cooperative learning is still a valuable teaching strategy for applying,and GI,LT,TGT and other cooperative learning modelare welcomed by classmates.