in this paper, tender problems in an automobile company for procuring needed items from potential suppliers have been resolved by the learning algorithm q. in this case the purchaser with respect to proposals received from potential providers, including price and delivery time is proposed; order the needed parts to suppliers assigns. the buyer’s objective is minimizing the procurement costs thr...

the error of inertial navigation systems increase versus time, therefore for achieving higher accuracy specially in long time navigations we have to use an aiding system. global positioning system is the best aiding system in this case. in this paper we first simulate a gps and ins; then simulate tightly integration and finally review adaptation method of kalman filtering a fuzzy adaptive kalma...

Background: High quality midwifery care for patients in the labor unit is of great importance in the promotion of women's wellbeing and pregnancy health. The aim of the present study was to evaluate the professional behavior of midwives with respect to their responsibilities and the service they provide in labor units in the university, governmental and private hospitals across Sari in 1390. M...

In Multi-Agent Reinforcement Learning, each agent observe a state of other agents as a part of environment. Therefore, the state space is exponential in the number of agents and learning speed significantly decrease. Modular Q-learning [6] needs very small state space. However, the incomplete observation involves a decline in the performance. In this paper, we improve Modular Q-learning’s perfo...

Multi–agent learning is a challenging open task in artificial intelligence. It is known an interesting connection between multi–agent learning algorithms and evolutionary game theory, showing that the learning dynamics of some algorithms can be modeled as replicator dynamics with a mutation term. Inspired by the recent sequence–form replicator dynamics, we develop a new version of theQ–learning...

We consider the problem of pan-tilt sensor control for active segmentation of incomplete multi-modal data. Since demanding optimal control does not allow for online replanning, we rather employ the optimal planner offline to provide guiding samples for learning a CNN-based control policy in a guided Q-learning framework. The proposed policy initialization and guided Q-learning avoids poor local...

We present Object Focused Q-learning (OF-Q), a novel reinforcement learning algorithm that can offer exponential speed-ups over classic Q-learning on domains composed of independent objects. An OF-Q agent treats the state space as a collection of objects organized into different object classes. Our key contribution is a control policy that uses non-optimal Q-functions to estimate the risk of ig...

Reinforcement learning agents interacting with a complex environment like the real world are unlikely to behave optimally all the time. If such an agent is operating in real-time under human supervision, now and then it may be necessary for a human operator to press the big red button to prevent the agent from continuing a harmful sequence of actions—harmful either for the agent or for the envi...