Consider an invertible n × n matrix over some field. The Gauss-Jordan elimination reduces this matrix to the identity matrix using at most n row operations and in general that many operations might be needed. In [1] the authors considered matrices in GL(n, q), the set of n × n invertible matrices in the finite field of q elements, and provided an algorithm using only row operations which perfor...

Abstract Reinforcement learning (RL) using deep Q-networks (DQNs) has shown performance beyond the human level in a number of complex problems. In addition, many studies have focused on bio-inspired hardware-based spiking neural networks (SNNs) given capabilities these technologies to realize both parallel operation and low power consumption. Here, we propose an on-chip training method for DQNs...

Query DAGs Adnan Darwiche Department of Mathematics American University of Beirut PO Box 11 236 Beirut, Lebanon [email protected] Gregory Provan Department of Diagnostics Rockwell Science Center 1049 Camino Dos Rios Thousand Oaks, Ca 91360 [email protected] Abstract This paper proposes a novel, algorithmindependent approach to optimizing belief network inference. Rather than designing ...

Coco (“cooperative/competitive”) values are a solution concept for two-player normalform games with transferable utility, when binding agreements and side payments between players are possible. In this paper, we show that coco values can also be defined for stochastic games and can be learned using a simple variant of Q-learning that is provably convergent. We provide a set of examples showing ...

Efficient propagation of information over a vehicular wireless network has usually remained the focus of the research community. Although, scanty contributions have been made in the field of vehicular data collection and more especially in applying learning techniques to such a very changing networking scheme. These smart learning approaches excel in making the collecting operation more reactiv...

We employ the Deep Q-Learning algorithm with Experience Replay to train an agent capable of achieving a high-level of play in the L-Game while selflearning from low-dimensional states. We also employ variable batch size for training in order to mitigate the loss of the rare reward signal and significantly accelerate training. Despite the large action space due to the number of possible moves, t...

This paper suggests a routing method for automated guided vehicles in port terminals that uses the Q-learning technique. One of the most important issues for the efficient operation of an automated guided vehicle system is to find shortest routes for the vehicles. In this paper, we determine shortest-time routes inclusive of the expected waiting times instead of simple shortest-distance routes,...

Episodic memory provides many important capabilities to a cognitive architecture. One of the challenges of creating a general episodic memory system is to be effective when given no information about the agent’s task. In this paper, we present an effective algorithm for detecting the relevance of the features of episodic memories while only being told when an agent completes a goal. We demonstr...