We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multiagent systems. Specifically, we focus on a novel action selection strategy for Q-learning (Watkins 1989). The new technique is applicable to scenarios where mutual observation of actions is not possible. To date, reinforcement learning approaches for such independent agents di...

We present a condition on the matrix of an underdetermined linear system which guarantees that the solution of the system with minimal `q-quasinorm is also the sparsest one. This generalizes, and sightly improves, a similar result for the `1-norm. We then introduce a simple numerical scheme to compute solutions with minimal `q-quasinorm, and we study its convergence. Finally, we display the res...

Despite the advancement of research and development on multi-robot teams, a key challenge still remains as to how to develop effective mechanisms that enable the robots to autonomously generate, adapt, and enhance team behaviours while improving their individual performance simultaneously. After a literature review of various multi-agent learning approaches, the two most promising learning para...

In realistic multiagent systems, learning on the basis of complete state information is not feasible. We introduce adaptive state focus Q-learning, a class of methods derived from Q-learning that start learning with only the state information that is strictly necessary for a single agent to perform the task, and that monitor the convergence of learning. If lack of convergence is detected, the l...

In this paper, we discuss Pro t-sharing, an experience-based reinforcement learning approach (which is similar to a Monte-Carlo based reinforcement learning method) that can be used to learn robust and e ective actions within uncertain, dynamic, multi-agent systems. We introduce the cut-loop routine that discards looping behavior, and demonstrate its e ectiveness empirically within a simpli ed ...

The software Q-system is designed to study the parallelism resource of numerical algorithms. By use Q-system, we can calculate any algorithm. Also, for set algorithms solving a given algorithmic problem, find an algorithm with best resource. theoretical basis concept Q-determinant where Q operations used by Any has and be represented in form Q-determinant. Such representation universal descript...

Intrusion Response is a relatively new field of research. Recent approaches for the creation Systems (IRSs) use Reinforcement Learning (RL) as primary technique optimal or near-optimal selection proper countermeasure to take in order stop mitigate an ongoing attack. However, most them do not consider fact that systems can change over time or, other words, exhibit non-stationary behaviors. Furth...