Search results for "Reinforcement"

Ultimate Strength and Fatigue Durability of Steel Reinforced Rubber Loading Hoses

2010

Loading hoses in an offshore loading buoy system in the North Sea were investigated with respect to extreme load resistance and fatigue durability. Both experimental work and fatigue life analyses were carried out. The FLS test is based on the principle of a service simulation test according to the American Petroleum Institute (API) 17B guidelines. The test results given in number of endured cycles from the laboratory test are scaled to the in-service conditions. Although the life estimate is based on one full scale test only, an attempt has been made to account for the inherent scatter in fatigue life. Furthermore, the results are validated by large test series with small scale test specim…

Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models

2017

This paper analyzes the emergent behaviors of pedestrian groups that learn through the multiagent reinforcement learning model developed in our group. Five scenarios studied in the pedestrian model literature, and with different levels of complexity, were simulated in order to analyze the robustness and the scalability of the model. Firstly, a reduced group of agents must learn by interaction with the environment in each scenario. In this phase, each agent learns its own kinematic controller, that will drive it at a simulation time. Secondly, the number of simulated agents is increased, in each scenario where agents have previously learnt, to test the appearance of emergent macroscopic beha…

A multi-agent system reinforcement learning based optimal power flow for islanded microgrids

2016

In this paper, a distributed intelligence algorithm is used to manage the optimal power flow problem in islanded microgrids. The methodology provides a suboptimal solution although the error is limited to a few percent as compared to a centralized approach. The solution algorithm is multi-agent based. According to the method, couples of agents communicate with each other only if the buses where they are located are electrically connected. The overall prizing system required for learning uses a feedback from an approximated model of the network. Based on the latter, a distributed reiforcement learning algorithm is implemented to minimize the joule losses while meeting operational constraints…

Robust Adaptive Modulation and Coding (AMC) selection in LTE systems using reinforcement learning

2014

Adaptive Modulation and Coding (AMC) in LTE networks is commonly employed to improve system throughput by ensuring more reliable transmissions. Most of existing AMC methods select the modulation and coding scheme (MCS) using pre-computed mappings between MCS indexes and channel quality indicator (CQI) feedbacks that are periodically sent by the receivers. However, the effectiveness of this approach heavily depends on the assumed channel model. In addition CQI feedback delays may cause throughput losses. In this paper we design a new AMC scheme that exploits a reinforcement learning algorithm to adjust at run-time the MCS selection rules based on the knowledge of the effect of previous AMC d…

Towards a Deep Reinforcement Learning Approach for Tower Line Wars

2017

There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relatively advanced computer games. There is undoubtedly an anticipation that Deep Reinforcement Learning will play a major role when the first AI masters the complicated game plays needed to beat a professional Real-Time Strategy game player. For this to be possible, there needs to be a game environment that targets and fosters AI research, and specifically Deep Reinforcement Learning. Some game environments already exist, however, these are either overly simplistic such as Atari 2600 or complex such as Starcraft II fro…

Learning Processes in the Control Theory

1994

The Reinforcement Effect of Strain Gauges Embedded in Low Modulus Materials

2013

The reinforcement effect of electrical resistance strain gauges is well-described in the literature, especially for strain gauges installed on surface. This paper considers the local reinforcement effect of strain gauges embedded within low Young modulus materials. In particular, by using a simple theoretical model, already used for strain gauges installed on the surface, it proposes a simple formula that allows the user to evaluate the local reinforcement effect of a generic strain gauge embedded on plastics, polymer composites, etc. The theoretical analysis has been integrated by numerical and experimental analyses, which confirmed the reliability of the proposed model.

On the Stiffness and the Reinforcement Effect of Electrical Resistance Strain Gauges

2006

The reinforcement effect of a strain gauge installed on low modulus materials can be significant. The increasing use of low modulus materials requires therefore the evaluation of such effect. This paper concerns the relationship between the local reinforcement effect and the strain gauge stiffness. The conclusion is that the gauge stiffness alone does not allow the user a thorough evaluation of the reinforcement effect.

Personality and reinforcement: An exploration using a maze-learning task

1995

A computerized maze learning task was investigated under control, reward and punishment, provided by differing financial reinforcement contingencies. The relationships between speed crossing the maze and anxiety and impulsivity personality traits were explored. Anxiety is hypothesized to reflect a behavioural inhibition system active in punishing environments; and impulsivity, to reflect an activation system active in rewarding environments. Of the measures of impulsivity taken, only one—venturesomeness from the I7—was associated significantly with increased maze crossing speed; this was found particularly in the reward condition and in males. Several anxiety variables were associated with …

Can Interpretable Reinforcement Learning Manage Prosperity Your Way?

2022

Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers’ needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and unde…