Deep Reinforcement Learning Approaches for the Game of Briscola

openReinforcement learning is increasingly becoming one of the most interesting areas of research in recent years. It is a machine learning approach that aims to design autonomous agents capable of learning from interaction with the envi- ronment, similar to how a human does. This peculiarity makes it particularly suitable for sequential decision making problems such as games. Indeed games are a perfect testing ground for reinforcement learning agents, due to a con- trolled environment, challenging tasks and a clear objective. Recent advances in deep learning allowed reinforcement learning algorithms to exceed human level performance in multiple games, the most notorious example being AlphaGo. In this thesis work we will apply deep reinforcement learning methods to Briscola, one of the most popular card games in Italy. After formalizing the two-player Briscola as a RL problem, we will apply two algorithms: Deep Q-learning and Proximal Policy Optimization. The agents will be trained against a random agent and an agent with predefined moves. The win rate will be used as a performance measure to compare the final results.Reinforcement learning is increasingly becoming one of the most interesting areas of research in recent years. It is a machine learning approach that aims to design autonomous agents capable of learning from interaction with the envi- ronment, similar to how a human does. This peculiarity makes it particularly suitable for sequential decision making problems such as games. Indeed games are a perfect testing ground for reinforcement learning agents, due to a con- trolled environment, challenging tasks and a clear objective. Recent advances in deep learning allowed reinforcement learning algorithms to exceed human level performance in multiple games, the most notorious example being AlphaGo. In this thesis work we will apply deep reinforcement learning methods to Briscola, one of the most popular card games in Italy. After formalizing the two-player Briscola as a RL problem, we will apply two algorithms: Deep Q-learning and Proximal Policy Optimization. The agents will be trained against a random agent and an agent with predefined moves. The win rate will be used as a performance measure to compare the final results

Development of passenger car equivalents for freeway merging sections

The Highway capacity manual (HCM, 2000) uses the Passenger Car Equivalents (PCE) to estimate the impact of trucks on freeways. PCE values for trucks recommended by HCM 2000 for level terrain are same under different truck percentages and different freeway conditions such as merging, diverging, weaving section etc. Several studies have shown that the PCE values vary for different traffic conditions such as for weaving, over-saturated conditions, etc. The objective of this thesis is to develop the PCE values for freeway merging sections using simulation software CORSIM. The equal density methodology is used to compute the PCE values. For this study PCE values are calculated for different traffic conditions including truck percentage, volume ratios (VR), and LOS. Analysis is also done with trucks on freeway only and ramp only; From the results of the study, estimated PCE values vary with level of service, truck percentage and volume ratio for merging section, adjacent upstream and downstream section. The study also shows that HCM overestimates the capacity of the merging sections. Since the results of this study are based on only one case study location, the PCE values developed may not be transferable to other locations and/or for other traffic conditions. However, the general relationship between the PCE values and traffic conditions such as percentage of trucks, VR, LOS are expected to be the similar. More extensive studies are recommended to validate the findings of this study

An Empirical Study to Examine the Role of Manufacturing Informatics in Smart Manufacturing

Smart Manufacturing(SM) being a technology plays a vital role in the enhancement of the performance of an industrial unit by incorporating different zones of engineering, both in terms of resources and informatics. Smart Manufacturing integrates manufacturing informatics (MIT) in real-time through an entire manufacturing process in medium and large companies. In the present era many reports are there that are dealing with the technical and operative characteristic of SM, but the role of manufacturing informatics as a vital element in the deployment of SM is not considered fully. Recognising the significance of role of MIT in SM, an empirical study has been carried out & presented in this paper with the purpose of achieving additional awareness

Finite Element Modeling and Validation of Steel Sheathed Cold-Formed Steel Framed Shear Walls

The objective of this paper is to validate the concept of utilizing a truss-element based finite element model for capturing the in-plane cyclic response of steel sheathed cold-formed steel (CFS) framed shear wall. The model is developed within the OpenSees finite element platform. Steel sheathed CFS shear walls show shear buckling of their sheathing as a tension field develops. This inelastic behavior of the shear walls is replicated by using the Pinching4 material for truss elements acting along the tension field. Importantly, the model employs beam-column elements for framing members, rotational springs for representing frame stiffness and vertical springs for modelling hold-downs. The wall models were calibrated using experimental data available for 0.030-in. and 0.033-in. steel sheet sheathed shear walls with 2:1 and 4:1 aspect ratios and 6-in., 4-in. and 2-in. fastener spacing at panel edges. The specimens were subjected to symmetric reverse cyclic displacement-controlled loading using the CUREE protocol. Comparison amongst the experimental and numerical models demonstrate a high degree of accuracy in the estimated shear strength and hysteretic response of the shear walls and as such has the potential to be an important building block towards modeling full structural systems constructed of cold-formed steel framing

The Impact of Sales Promotion on Brand Equity: The Case of Brewery Industry

The effects of sales promotion on the creation of brand equity; effects of brand equity dimensions on overall brand equity; and the nature of inter-relationship among brand equity dimensions is a new area of research in Ethiopia. By adapting exploratory approach, the study assumed a positive sales promotion-brand equity dimensions-overall brand equity linkage in the Ethiopian beer market. Structural equation modeling (SEM) was used to verify the conceptual model, that is, the hypothesized linkage. The study is purely quantitative and a cross-sectional descriptive research design was applied. The study confirmed brand equity is a multidimensional concept that consists of brand loyalty, perceived quality and brand associations. Brand loyalty exerts a great influence on the formulation of brand equity and it is a holistic concept. The nature of brand equity dimensions relationship in the Ethiopian brewery industry is a causal order.  The study also indicated that sales promotion affecting the formulation of brand equity with different level of intensity. The study further concluded that monetary promotion affecting positively the creation of brand equity by influencing brand awareness, brand associations and perceived quality; and non-monetary sales promotion affecting positively the formulation of brand equity by influencing brand awareness and negatively by affecting perceived quality. Keywords: Brand Equity, Brand Loyalty, Perceived Quality, Brand Associations, Brand Awareness, Sales Promotion, Ethiopi