the analyzing methods of texts and their rendering are diverse and wide spread. this diversity spreads according to the renderers’ approach and viewpoint to premises and intellectual ‚ scientific and literary issues and to the extent of their  skill and knowledge in their specialized learning's . therefore they survey there issues from their viewpoints and trainings from this aspect we gra...

The population-ecology approach (the natural-selection model) used to be a major orientation in consideration of organizational change and transformation. It is presented by its proponents as a theory distinct from structural contingency theory. This theory begins by rejecting the claim of contingency theory that organizations are adaptive. Contingency theory understands that the social and bus...

The primary education sector in Pakistan faces many challenges relating to access to education and quality of resources. This paper evaluates the Educational Voucher Scheme (EVS) in Lahore, Pakistan aimed at increasing access to primary schooling for low income families residing in the underdeveloped areas of Lahore by using the four criteria for evaluating privatization plans in education outl...

Recent work has shown that Deep Q-Networks (DQNs) are capable of learning human-level control policies on a variety of different Atari 2600 games [1]. Other work has looked at treating the Atari problem as a partially observable Markov decision process (POMDP) by adding imperfect state information through image flickering [2]. However, these approaches leverage a convolutional network structure...

Recently, reinforcement learning has been successfully applied to the logical game of Go, various Atari games, and even a 3D game, Labyrinth, though it continues to have problems in sparse reward settings. It is difficult to explore, but also difficult to exploit, a small number of successes when learning policy. To solve this issue, the subgoal and option framework have been proposed. However,...

Counterfactual explanations, which deal with "why not?" scenarios, can provide insightful explanations to an AI agent's behavior. In this work, we focus on generating counterfactual for deep reinforcement learning (RL) agents operate in visual input environments like Atari. We introduce state a novel example-based approach based generative learning. Specifically, illustrates what minimal change...

Recent progress in Reinforcement Learning (RL), fueled by its combination, with Deep Learning has enabled impressive results in learning to interact with complex virtual environments, yet real-world applications of RL are still scarce. A key limitation is data efficiency, with current state-of-the-art approaches requiring millions of training samples. A promising way to tackle this problem is t...

This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, called human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This is meant to compensate for the difficulties of current exploration...