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Making an effective school and classroom management in particular is indeed a challenge for both the director and the teachers. Because, it is necessary to consider the action of continuous regulation and efficiency of the communicative process, transformation of the institution into an inclusive organization in the sense of knowing, understanding the differences that arise from the multiple interactions between the people involved in the educational process, irrespective of the hierarchical line , but especially in order to exploit and maximize these differences in the direction of increasing the efficiency and productivity of work at both individual and collective level.

StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the requirements for expert knowledge and enhance the coordination of the systematic bot, we select reinforcement learning (RL) to tackle the problem of macromanagement. We propose a novel deep RL method, Mean Asynchronous Advantage Actor-Critic (MA3C), which computes the approximate expected policy gradient instead of the gradient of sampled action to reduce the variance of the gradient, and encode the history queue with recurrent neural network to tackle the problem of imperfect information. The experimental results show that MA3C achieves a very high rate of winning, approximately 90%, against the weaker opponents and it improves the win rate about 30% against the stronger opponents. We also propose a novel method to visualize and interpret the policy learned by MA3C. Combined with the visualized results and the snapshots of games, we find that the learned macromanagement not only adapts to the game rules and the policy of the opponent bot, but also cooperates well with the other modules of MA3C-Bot.

Making an effective school and classroom management in particular is indeed a challenge for both the director and the teachers. Because, it is necessary to consider the action of continuous regulation and efficiency of the communicative process, transformation of the institution into an inclusive organization in the sense of knowing, understanding the differences that arise from the multiple interactions between the people involved in the educational process, irrespective of the hierarchical line, but especially in order to exploit and maximize these differences in the direction of increasing the efficiency and productivity of work at both individual and collective level. [ABSTRACT FROM AUTHOR]

This article critically examines the origins and evolution of China’s unique land institutions and situates land policy in the larger context of China’s reforms and pursuit of economic growth. It argues that the Chinese Communist Party (CCP) has strengthened the institutions that permit land expropriation—namely, urban/rural dualism, decentralized land ownership, and hierarchical land management—in order to use land as a key instrument of macroeconomic regulation, helping the CCP respond to domestic and international economic trends and manage expansion and contraction. Key episodes of macroeconomic policymaking are analyzed, with the use of local and central documents, to show how the CCP relied on the manipulation and distribution of the national land supply either to stimulate economic growth or to rein in an overheating economy. China’s land institutions, therefore, share “complementarities” with fiscal and financial institutions and benefit powerful political actors while imposing costs on marginal ones. [ABSTRACT FROM AUTHOR]

StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e., selecting appropriate units to build depending on the current state, is one of the most important problems in this game. To reduce the requirements for expert knowledge and enhance the coordination of the systematic bot, we select reinforcement learning (RL) to tackle the problem of macromanagement. We propose a novel deep RL method, Mean Asynchronous Advantage Actor-Critic (MA3C), which computes the approximate expected policy gradient instead of the gradient of sampled action to reduce the variance of the gradient, and encode the history queue with recurrent neural network to tackle the problem of imperfect information. The experimental results show that MA3C achieves a very high rate of winning, approximately 90%, against the weaker opponents and it improves the win rate about 30% against the stronger opponents. We also propose a novel method to visualize and interpret the policy learned by MA3C. Combined with the visualized results and the snapshots of games, we find that the learned macromanagement not only adapts to the game rules and the policy of the opponent bot, but also cooperates well with the other modules of MA3C-Bot. [ABSTRACT FROM AUTHOR]