Relational representations have great potential for rapidly generalizing learned knowledge in large Markov decision processes such as multi-agent problems. In this work, we introduce relational temporal difference learning for the distributed case where the communication links among agents are dynamic. Thus no critical components of the system should reside in any one agent. Relational generali...

We propose a distributed deep learning model to successfully learn control policies directly from highdimensional sensory input using reinforcement learning. The model is based on the deep Q-network, a convolutional neural network trained with a variant of Q-learning. Its input is raw pixels and its output is a value function estimating future rewards from taking an action given a system state....

Many researchers who study stochastic simulations such as reinforcement learning algorithms have to run their programs many times to compare developing algorithms and find better sets of parameters for their programs. In order to reduce their working time, we build a problem solving environments (PSE) system to assist them. Our system has three sub-systems: a distributed computing system, a dat...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

nowadays, outsourcing is viewed as a trade strategy and organizations tend to adopt new strategies to achieve competitive advantages in the current world of business. focusing on main copmpetencies, and transferring most of activities to outside resources of organization( outsourcing) is one such strategy is. in this paper, we aim to decide on decision maker agent of transportation system, by a...

Learning from distributed data is becoming in our times a necessity, but it is also a complex and challenging task. Approaches developed so far have not dealt with the uncertainty, imprecision and vagueness involved in distributed learning. Meta-Learning, a successful approach for distributed data mining, is in this paper extended to handle the imprecision and uncertainty of the local models an...