Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

Metallic glass fiber-reinforced Zr-based bulk metallic glass

A novel Zr-based bulk metallic glass composite was fabricated using stainless steel capillaries as the reinforcement. Large plasticity (14%) was achieved in the composite with a reinforcement volume fraction of 38%. The high plasticity observed can be attributed to the formation of small glass fibers encapsulated by the steel capillaries, which promotes multiple shear bands in both metallic glass matrix and the fibers themselves. A new parameter was also proposed to approximately evaluate the reinforcement efficiency.

Application of reinforcement learning in an open railway access market price negotiation

In an open railway access market price negotiation, it is feasible to achieve higher cost recovery by applying the principles of price discrimination. The price negotiation can be modeled as an optimization problem of revenue intake. In this paper, we present the pricing negotiation based on reinforcement learning model. A negotiated-price setting technique based on agent learning is introduced, and the feasible applications of the proposed method for open railway access market simulation are discussed.

Effect of spacing of reinforcement on the behaviour of partially grouted masonry shear walls

Partially Grouted Reinforced Masonry (PGRM) shear walls perform well in places where the cyclonic wind pressure dominates the design. Their out-of-plane flexural performance is better understood than their inplane shear behaviour; in particular, it is not clear whether the PGRM shear walls act as unreinforced masonry (URM) walls embedded with discrete reinforced grouted cores or as integral systems of reinforced masonry (RM) with wider spacing of reinforcement. With a view to understanding the inplane response of PGRM shear walls, ten full scale single leaf, clay block walls were constructed and tested under monotonic and cyclic inplane loading cases. It has been shown that where the spacing of the vertical reinforcement is less than 2000mm, the walls behave as an integral system of RM; for spacing greater than 2000mm, the walls behave similar to URM with no significant benefit from the reinforced cores based on the displacement ductility and stiffness degradation factors derived from the complete lateral load – lateral displacement curves.

Encyclopedia contributions : entries S-Z in Oxford Companion to Australian Gardens

Shelton, E.M. (p.548); Sherwood Arboretum (p.550); Soutter, William (pp.563-4); Styles (pp.575-6); Summer-House (579-580); Trapnell, W.G. (p.602); Tropical Gardens (pp.604-5);Verandah Gardening (p.614); Wickham Park (p.642); Wijaya, Made (p.642); Williams, George (p.644); Williams Keith A (p.644).

REGAL : a regularization based algorithm for reinforcement learning in weakly communicating MDPs

We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each episode, it picks a policy using regularization based on the span of the optimal bias vector. For an MDP with S states and A actions whose optimal bias vector has span bounded by H, we show a regret bound of ~ O(HS p AT ). We also relate the span to various diameter-like quantities associated with the MDP, demonstrating how our results improve on previous regret bounds.