Tootfinder

Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
Eura Nofshin, Siddharth Swaroop, Weiwei Pan, Susan Murphy, Finale Doshi-Velez
https://arXiv.org/abs/2401.14923

Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
Many important behavior changes are frictionful; they require individuals to expend effort over a long period with little immediate gratification. Here, an artificial intelligence (AI) agent can provide personalized interventions to help individuals stick to their goals. In these settings, the AI agent must personalize rapidly (before the individual disengages) and interpretably, to help us understand the behavioral interventions. In this paper, we introduce Behavior Model Reinforcement Learnin…

Enhancing Software Effort Estimation through Reinforcement Learning-based Project Management-Oriented Feature Selection
Haoyang Chen, Botong Xu, Kaiyang Zhong
https://arxiv.org/abs/2403.16749

Enhancing Software Effort Estimation through Reinforcement Learning-based Project Management-Oriented Feature Selection
Purpose: The study aims to investigate the application of the data element market in software project management, focusing on improving effort estimation by addressing challenges faced by traditional methods. Design/methodology/approach: This study proposes a solution based on feature selection, utilizing the data element market and reinforcement learning-based algorithms to enhance the accuracy of software effort estimation. It explores the application of the MARLFS algorithm, customizing impr…

This https://arxiv.org/abs/2402.16063 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Citation-Enhanced Generation for LLM-based Chatbot
Large language models (LLMs) exhibit powerful general intelligence across diverse scenarios, including their integration into chatbots. However, a vital challenge of LLM-based chatbots is that they may produce hallucinated content in responses, which significantly limits their applicability. Various efforts have been made to alleviate hallucination, such as retrieval augmented generation and reinforcement learning with human feedback, but most of them require additional training and data annota…

"""
The case of One Laptop per Child shows us why it is dangerous to ignore the origins of charisma: one risks being perpetually entranced by the newest charismatic technology. This is not to say that cultural change with a technology-centric project is impossible. Still, even more realistic reforms grounded in the realities of their intended beneficiaries sometimes have difficulty gaining broad popular support outside the school unless they add a charismatic gloss of rapid, revolutionary change.
This charismatic pressure can put even open-eyed reformers in a catch-22. They must promise dramatic results to gain the social and financial support for reforms, and then they must either admit to not achieving their goals or pretend that they did achieve them. Either way, funders will declare that the project is finished and withdraw financial support, and then researchers and other observers will begin to note the discrepancies between reformers’ promises and their own observations. Thus, projects that rely on charismatic technologies are often short lived; their resources are cut off before charisma recedes into the background and before the technology becomes part of everyday classroom experience. This catch-22 has dogged efforts for educational reform, development, and cultural change — especially those funded through grants or other short-term funding — for well over a century. As the technology community moves on to the next charismatic device without learning from its failures, this will continue to hamper the possibility of real, if incremental, change.
[…] After all, charisma is ultimately a conservative social force. Even when charismatic technologies promise to quickly and painlessly transform our lives for the better, they appeal precisely because they echo existing stereotypes, confirm the value of existing power relations, and reinforce existing ideologies. Meanwhile, they may divert attention and resources from more complicated, expensive, or politically charged reforms that do not promise a quick fix and are thus less charismatic.
"""
(Morgan G. Ames, The Charisma Machine)

Beacon, a lightweight deep reinforcement learning benchmark library for flow control
Jonathan Viquerat, Philippe Meliga, Pablo Jeken, Elie Hachem
https://arxiv.org/abs/2402.17402 …

Beacon, a lightweight deep reinforcement learning benchmark library for flow control
Recently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing algorithms to the control of numerical fluid dynamics environments. Although still in its infancy, the field has seen multiple successes in a short time span, and its fast development pace can certainly be partly imparted to the open-source effort that drives the expansion of the community. Yet, this emerging domain s…

"""
The case of One Laptop per Child shows us why it is dangerous to ignore the origins of charisma: one risks being perpetually entranced by the newest charismatic technology. This is not to say that cultural change with a technology-centric project is impossible. Still, even more realistic reforms grounded in the realities of their intended beneficiaries sometimes have difficulty gaining broad popular support outside the school unless they add a charismatic gloss of rapid, revolutionary change.
This charismatic pressure can put even open-eyed reformers in a catch-22. They must promise dramatic results to gain the social and financial support for reforms, and then they must either admit to not achieving their goals or pretend that they did achieve them. Either way, funders will declare that the project is finished and withdraw financial support, and then researchers and other observers will begin to note the discrepancies between reformers’ promises and their own observations. Thus, projects that rely on charismatic technologies are often short lived; their resources are cut off before charisma recedes into the background and before the technology becomes part of everyday classroom experience. This catch-22 has dogged efforts for educational reform, development, and cultural change — especially those funded through grants or other short-term funding — for well over a century. As the technology community moves on to the next charismatic device without learning from its failures, this will continue to hamper the possibility of real, if incremental, change.
[…] After all, charisma is ultimately a conservative social force. Even when charismatic technologies promise to quickly and painlessly transform our lives for the better, they appeal precisely because they echo existing stereotypes, confirm the value of existing power relations, and reinforce existing ideologies. Meanwhile, they may divert attention and resources from more complicated, expensive, or politically charged reforms that do not promise a quick fix and are thus less charismatic.
"""
(Morgan G. Ames, The Charisma Machine)

Measuring Exploration in Reinforcement Learning via Optimal Transport in Policy Space
Reabetswe M. Nkhumise, Debabrota Basu, Tony J. Prescott, Aditya Gilra
https://arxiv.org/abs/2402.09113

Measuring Exploration in Reinforcement Learning via Optimal Transport in Policy Space
Exploration is the key ingredient of reinforcement learning (RL) that determines the speed and success of learning. Here, we quantify and compare the amount of exploration and learning accomplished by a Reinforcement Learning (RL) algorithm. Specifically, we propose a novel measure, named Exploration Index, that quantifies the relative effort of knowledge transfer (transferability) by an RL algorithm in comparison to supervised learning (SL) that transforms the initial data distribution of RL t…

Raiders to continue exploring options to trade veteran WR https://www.yardbarker.com/nfl/articles/raiders_to_continue_exploring_options_to_trade_veteran_wr/s1_14819_39983711

Raiders to continue exploring options to trade veteran WR
The Raiders will continue their efforts to trade wide receiver Hunter Renfrow, as Vincent Bonsignore of the Las Vegas Review-Journal writes.  The club tried to deal Renfrow in advance of the 2023 trade deadline, but the guaranteed money that remained on the slot man’s contract proved to be a deterrent for interested teams. Now, however, the only money that remains on Renfrow’s deal is his non-guaranteed base salary of $11.15M.  As the Clemson product is coming off of back-to-back disappoi…

Die Perle im Norden?
https://weddingweiser.de/siedlung-im-dornroeschenschlaf/

Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning
Linghang Sun, Michail A. Makridis, Alexander Genser, Cristian Axenie, Margherita Grossi, Anastasios Kouvelas
https://arxiv.org/abs/2402.12665

Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning
The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that struggle to cope with real-world uncertainties, leading to a significant decline in effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to improving the robustness or resilience of transportation systems against disruptions, this pa…