nowadays project management is a key component in introductory operations management. the educators and the researchers in these areas advocate representing a project as a network and applying the solution approaches for network models to them to assist project managers to monitor their completion. in this paper, we evaluated project’s completion time utilizing the q-learning algorithm. so the ...

Anxiety disorders are the most common reasons for referring to specialized clinics. If the response to stress changed, anxiety can be greatly controlled. The most obvious effect of stress occurs on circulatory system especially through sweating. the electrical conductivity of skin or in other words Galvanic Skin Response (GSR) which is dependent on stress level is used; beside this parameter pe...

Nowadays project management is a key component in introductory operations management. The educators and the researchers in these areas advocate representing a project as a network and applying the solution approaches for network models to them to assist project managers to monitor their completion. In this paper, we evaluated project’s completion time utilizing the Q-learning algorithm. So the ...

This paper presents the HDG learning algorithm, which uses a hierarchical decomposition of the state space to make learning to achieve goals more efficient with a small penalty in path quality. Special care must be taken when performing hierarchical planning and learning in stochastic domains, because macro-operators cannot be executed ballistically. The HDG algorithm, which is a descendent of ...

Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of a safe-wandering task with the autonomous micro-robots, Alice. We propose a simplified reinforcement learning algorithm based on one-step Q-learning that is op...

This paper investigates the potential of flat and hierarchical reinforcement learning (HRL) for solving problems within strategy games. A HRL method, Max-Q, is applied to a unit transportation task modelled within a simplified, discrete real-time strategy game engine, and its performance compared to that of flat Q-learning. It is shown that reinforcement learning approaches, and especially hier...

The research on deep reinforcement learning which estimates Q-value by deep learning has been attracted the interest of researchers recently. In deep reinforcement learning, it is important to efficiently learn the experiences that an agent has collected by exploring environment. In this research, we propose NEC2DQN that improves learning speed of a poor sample efficiency algorithm such as DQN ...

Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e.g., sandwich making and playing the video games). In this paper, we leverage Reinforcement Learning (RL) to highlight task-relevant locations of input frames. We propose a soft attention mechanism combined with the Deep Q-Network (DQN) model to teach an RL agent h...

In this paper we derive convergence rates for Q-learning. We show an interesting relationship between the convergence rate and the learning rate used in Q-learning. For a polynomial learning rate, one which is 1/t at time t where ω ∈ (1/2, 1), we show that the convergence rate is polynomial in 1/(1− γ), where γ is the discount factor. In contrast we show that for a linear learning rate, one whi...