An efficient genetic reinforcement learning algorithm for designing Fuzzy Inference System (FIS) with out any priory knowledge is proposed in this paper. Reinforcement learning using Fuzzy Q-Learning (FQL) is applied to select the consequent action values of a fuzzy inference system, in this method, the consequent value is selected from a predefined value set which is kept unchanged during lear...

Distributed drive electric vehicles are regarded as the promising transportation due to advanced power flow architecture. Optimizing yaw motion enhance vehicle safety is a challenging job. Besides, nonlinear features in affect control accuracy of controllers. To this end, deep reinforcement learning (DRL) based direct moment (DYC) strategy put forward here. Vehicle dynamics can be approximated ...

With the development of edge-cloud computing technologies, distributed data centers (DCs) have been extensively deployed across global Internet. Since different users/applications heterogeneous requirements on specific types ICT resources in DCs, how to optimize such under dynamic and even uncertain environments becomes a challenging issue. Traditional approaches are not able provide effective ...

Abstract The exponential device growth in industrial Internet of things (IIoT) has a noticeable impact on the volume data generated. Edge-cloud computing cooperation been introduced to IIoT lessen computational load cloud servers and shorten processing time for data. General programmable logic controllers (PLCs), which have playing important roles control systems, start gain ability process lar...

We present analog VLSI neuromorphic architectures for a general class of learning tasks, which include supervised learning, reinforcement learning, and temporal difference learning. The presented architectures are parallel, cellular, sparse in global interconnects, distributed in representation, and robust to noise and mismatches in the implementation. They use a parallel stochastic perturbatio...

In both research fields, Case-Based Reasoning and Reinforcement Learning, the system under consideration gains its expertise from experience. Utilizing this fundamental common ground as well as further characteristics and results of these two disciplines, in this paper we develop an approach that facilitates the distributed learning of behaviour policies in cooperative multi-agent domains witho...

In large, distributed systems such as mobilized ad-hoc networks, centralized learning of routing or movement policies may be impractical. We need to employ multi-agent learning algorithms that can learn independently, without the need for extensive coordination. Using only a simple coordination signals such as a global reward value, we show that reinforcement learning methods can be used to con...

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of th...