In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic no...In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic nonlinear systems. The idea is to use an iterative adaptive dynamic programming(ADP) technique to construct the iterative control law which optimizes the iterative Q function. When the optimal Q function is obtained, the optimal control law can be achieved by directly minimizing the optimal Q function, where the mathematical model of the system is not necessary. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. It is also proven that any of the iterative control laws is a stable control law. Neural networks are employed to implement the policy iteration based deterministic Q-learning algorithm, by approximating the iterative Q function and the iterative control law, respectively. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm.展开更多
The existing literature has revealed that Problem-based Learning （PBL） can improve the cognitive competence of learners, but few studies focus on L2 learning from the perspective of students, or on the relationship ...The existing literature has revealed that Problem-based Learning （PBL） can improve the cognitive competence of learners, but few studies focus on L2 learning from the perspective of students, or on the relationship between PBL and linguistic cognition. Based on students＇ reflective journals, the researcher＇s observation notes, and interviews with teachers and students, this case study describes the individual and collective self-negotiations during a Problem-Based L2 Learning （PBLL） practice of 157 non-English majors at three universities in Beijing. The current study makes a distinction between surface and deep self-negotiations, and confirms the conception of the self-negotiated L2 cognition of PBLL learners. The research results show （1） that the self-negotiation is a consistent feature of PBLL because the whole PBLL process comprises the cyclic intertwining of individual and collective self-negotiations, （2） that L2 learners manage to achieve individual and collective self-negotiations through cognitive mechanisms of linking, riffling and converging, and （3） that deep self-negotiations in PBLL are more dynamic, interactive, and generative. Pedagogical implications, research limitations, and future directions are also discussed.展开更多
基金supported in part by National Natural Science Foundation of China(Grant Nos.6137410561233001+1 种基金61273140)in part by Beijing Natural Science Foundation(Grant No.4132078)
文摘In this paper, a novel iterative Q-learning algorithm, called "policy iteration based deterministic Qlearning algorithm", is developed to solve the optimal control problems for discrete-time deterministic nonlinear systems. The idea is to use an iterative adaptive dynamic programming(ADP) technique to construct the iterative control law which optimizes the iterative Q function. When the optimal Q function is obtained, the optimal control law can be achieved by directly minimizing the optimal Q function, where the mathematical model of the system is not necessary. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. It is also proven that any of the iterative control laws is a stable control law. Neural networks are employed to implement the policy iteration based deterministic Q-learning algorithm, by approximating the iterative Q function and the iterative control law, respectively. Finally, two simulation examples are presented to illustrate the performance of the developed algorithm.
基金sponsored by the Program in Social Sciences of Beijing Municipal Commission of Education (SM201511232008)
文摘The existing literature has revealed that Problem-based Learning （PBL） can improve the cognitive competence of learners, but few studies focus on L2 learning from the perspective of students, or on the relationship between PBL and linguistic cognition. Based on students＇ reflective journals, the researcher＇s observation notes, and interviews with teachers and students, this case study describes the individual and collective self-negotiations during a Problem-Based L2 Learning （PBLL） practice of 157 non-English majors at three universities in Beijing. The current study makes a distinction between surface and deep self-negotiations, and confirms the conception of the self-negotiated L2 cognition of PBLL learners. The research results show （1） that the self-negotiation is a consistent feature of PBLL because the whole PBLL process comprises the cyclic intertwining of individual and collective self-negotiations, （2） that L2 learners manage to achieve individual and collective self-negotiations through cognitive mechanisms of linking, riffling and converging, and （3） that deep self-negotiations in PBLL are more dynamic, interactive, and generative. Pedagogical implications, research limitations, and future directions are also discussed.
