Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting. To further investigate the properties and feasibility on real-world applications, this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a novel reinforcement learning (RL) benchmark that aims at being realistic by including a variety of aspects found in industrial applications, like continuous state and action spaces, a high dimensional, partially observable state space, delayed effects, and complex stochasticity. The experimental results of PSO-P on IB are compared to results of closed-form control policies derived from the model-based Recurrent Control Neural Network (RCNN) and the model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not only of interest for academic benchmarks, but also for real-world industrial applications, since it also yielded the best performing policy in our IB setting. Compared to other well established RL techniques, PSO-P produced outstanding results in performance and robustness, requiring only a relatively low amount of effort in finding adequate parameters or making complex design decisions

Does goal setting training and self-management training increase self-efficacy in negotiation even in the presence of a negotiation stereotype threat

With women gaining more knowledge and asking for more money, the disparity in the salary gap is starting to close. However, women are still getting paid less. It has been found that women generally do not negotiate as high of salaries as men (Mazei, Huffmeier, Freund, Stuhlmacher, Bilke, & Hertel, 2015). In fact, when women were reminded of the stereotype threat surrounding women and negotiation, they often had lower negotiated salaries and salary goals (Tellhed & Bjrklund, 2011; Kray, Thompson, & Galinsky, 2001). However, Gist, Stevens, and Bavetta (1991) found that salary negotiation performance was strongly, positively related to self-efficacy. The present study will examine whether goal setting training and self-management training will increase negotiation self-efficacy. We also want to examine the effect that well known negotiation stereotypes have on self-efficacy and salary goals

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems

Targeting of IL-2 to cytotoxic lymphocytes as an improved method of cytokine-driven immunotherapy

The use of high-dose interleukin-2 (IL-2) has fallen out of favor due to severe life-threatening side effects. We have recently described a unique way of directly targeting IL-2 to cytotoxic lymphocytes using a virally encoded immune evasion protein and an IL-2 mutant that avoids off-target side effects such as activation of regulatory T cells and vascular endothelium

Implementing Scaled-Agile Frameworks at Non-Digital Born Companies - A Multiple Case Study

For traditional enterprises to harness the advantages of organizational agility, scaled-agile frameworks seem to be more appropriate to adopt agile practices at large scale. However, the adoption of agile practices often creates trade-offs between the implementation of an ideal theoretical framework and company-specific necessities. While extant research has covered the implications and challenges when adopting agile structures, our research focuses on the how and why of such trade-offs using Socio-Technical Systems Theory. Drawing on the results of an exploratory multiple case study, we reveal that companies either choose a top-down or bottom-up approach for implementation. While the first often is triggered by the need to increase customer centricity, the latter is mostly triggered by the need to increase the number of releases. Moreover, we found that the selected implementation approach has significant impact on the key design parameters for and the content of the implementation of scaled-agile frameworks

Does a measure of adaptive performance predict in-basket performance?

The ability for employees to adapt to a given situation is critical for organizations hoping to remain successful in today’s turbulent environment. For law enforcement officers, adapting to a situation is even more critical since this can be the difference between life or death. In an effort to assist law enforcement in promoting personnel with the adaptive performance (AP) skill required to be effective, this study examines the relationship between AP and an individual’s performance on an in-basket assessment. The study found that AP significantly predicts in-basket performance when rank is considered

Innovative method for cutting edge preparation with flexible diamond tools

The micro geometry of the cutting edge is of central importance for the performance of cutting tools. It influences all essential parameters in the machining process: chip formation, thermal and mechanical load on the tool and the workpiece, tool wear and the resulting workpiece quality. The effect depends on the size and shape of the cutting edge rounding. Depending on the machining process, asymmetrical roundings often show the greatest potential. In addition to increasing tool life, the quality of the surfaces produced can be improved by a specifically designed asymmetrical rounding. For edge preparation, blasting, brushing and drag finishing are used in industrial applications. However, an economic production of asymmetrical cutting edge geometries on cutting tools with complicated cutting edge geometry, such as solid carbide tools with helical cutting edge, cannot be achieved with these methods. Therefore, a novel method for preparation of the cutting edge rounding using flexible bond diamond polishing tools is introduced. Hence, the conducted research in this study analyzes the basic mechanisms and influencing factors using the new preparation method. For this purpose, polishing tests are carried out on carbide indexable inserts. The results show that the polishing tools can be used to create both asymmetrical and symmetrical roundings in an industrially relevant dimension. © 2019 The Authors. Published by Elsevier B.V