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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.03820">arXiv:2411.03820</a> <span> [<a href="https://arxiv.org/pdf/2411.03820">pdf</a>, <a href="https://arxiv.org/format/2411.03820">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Beyond The Rainbow: High Performance Deep Reinforcement Learning On A Desktop PC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Clark%2C+T">Tyler Clark</a>, <a href="/search/cs?searchtype=author&query=Towers%2C+M">Mark Towers</a>, <a href="/search/cs?searchtype=author&query=Evers%2C+C">Christine Evers</a>, <a href="/search/cs?searchtype=author&query=Hare%2C+J">Jonathon Hare</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.03820v1-abstract-short" style="display: inline;"> Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance. In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL using a desktop PC, with a human-normalized… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03820v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03820v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03820v1-abstract-full" style="display: none;"> Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance. In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates six improvements from across the RL literature to Rainbow DQN, establishing a new state-of-the-art for RL using a desktop PC, with a human-normalized interquartile mean (IQM) of 7.4 on atari-60. Beyond Atari, we demonstrate BTR's capability to handle complex 3D games, successfully training agents to play Super Mario Galaxy, Mario Kart, and Mortal Kombat with minimal algorithmic changes. Designing BTR with computational efficiency in mind, agents can be trained using a desktop PC on 200 million Atari frames within 12 hours. Additionally, we conduct detailed ablation studies of each component, analzying the performance and impact using numerous measures. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03820v1-abstract-full').style.display = 'none'; document.getElementById('2411.03820v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 main pages, 26 total. Currently under review at ICLR</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.08230">arXiv:2408.08230</a> <span> [<a href="https://arxiv.org/pdf/2408.08230">pdf</a>, <a href="https://arxiv.org/format/2408.08230">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Explaining an Agent's Future Beliefs through Temporally Decomposing Future Reward Estimators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Towers%2C+M">Mark Towers</a>, <a href="/search/cs?searchtype=author&query=Du%2C+Y">Yali Du</a>, <a href="/search/cs?searchtype=author&query=Freeman%2C+C">Christopher Freeman</a>, <a href="/search/cs?searchtype=author&query=Norman%2C+T+J">Timothy J. Norman</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.08230v1-abstract-short" style="display: inline;"> Future reward estimation is a core component of reinforcement learning agents; i.e., Q-value and state-value functions, predicting an agent's sum of future rewards. Their scalar output, however, obfuscates when or what individual future rewards an agent may expect to receive. We address this by modifying an agent's future reward estimator to predict their next N expected rewards, referred to as Te… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08230v1-abstract-full').style.display = 'inline'; document.getElementById('2408.08230v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.08230v1-abstract-full" style="display: none;"> Future reward estimation is a core component of reinforcement learning agents; i.e., Q-value and state-value functions, predicting an agent's sum of future rewards. Their scalar output, however, obfuscates when or what individual future rewards an agent may expect to receive. We address this by modifying an agent's future reward estimator to predict their next N expected rewards, referred to as Temporal Reward Decomposition (TRD). This unlocks novel explanations of agent behaviour. Through TRD we can: estimate when an agent may expect to receive a reward, the value of the reward and the agent's confidence in receiving it; measure an input feature's temporal importance to the agent's action decisions; and predict the influence of different actions on future rewards. Furthermore, we show that DQN agents trained on Atari environments can be efficiently retrained to incorporate TRD with minimal impact on performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08230v1-abstract-full').style.display = 'none'; document.getElementById('2408.08230v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages + 3 pages of supplementary material. Published at ECAI 2024</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> ECAI 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.17032">arXiv:2407.17032</a> <span> [<a href="https://arxiv.org/pdf/2407.17032">pdf</a>, <a href="https://arxiv.org/format/2407.17032">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Digital Libraries">cs.DL</span> </div> </div> <p class="title is-5 mathjax"> Gymnasium: A Standard Interface for Reinforcement Learning Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Towers%2C+M">Mark Towers</a>, <a href="/search/cs?searchtype=author&query=Kwiatkowski%2C+A">Ariel Kwiatkowski</a>, <a href="/search/cs?searchtype=author&query=Terry%2C+J">Jordan Terry</a>, <a href="/search/cs?searchtype=author&query=Balis%2C+J+U">John U. Balis</a>, <a href="/search/cs?searchtype=author&query=De+Cola%2C+G">Gianluca De Cola</a>, <a href="/search/cs?searchtype=author&query=Deleu%2C+T">Tristan Deleu</a>, <a href="/search/cs?searchtype=author&query=Goul%C3%A3o%2C+M">Manuel Goul茫o</a>, <a href="/search/cs?searchtype=author&query=Kallinteris%2C+A">Andreas Kallinteris</a>, <a href="/search/cs?searchtype=author&query=Krimmel%2C+M">Markus Krimmel</a>, <a href="/search/cs?searchtype=author&query=KG%2C+A">Arjun KG</a>, <a href="/search/cs?searchtype=author&query=Perez-Vicente%2C+R">Rodrigo Perez-Vicente</a>, <a href="/search/cs?searchtype=author&query=Pierr%C3%A9%2C+A">Andrea Pierr茅</a>, <a href="/search/cs?searchtype=author&query=Schulhoff%2C+S">Sander Schulhoff</a>, <a href="/search/cs?searchtype=author&query=Tai%2C+J+J">Jun Jet Tai</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+H">Hannah Tan</a>, <a href="/search/cs?searchtype=author&query=Younis%2C+O+G">Omar G. Younis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.17032v3-abstract-short" style="display: inline;"> Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment and algorithm implementations. This makes it difficult for researchers to compare and build upon each other's work, slowing down progress in the field. Gym… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.17032v3-abstract-full').style.display = 'inline'; document.getElementById('2407.17032v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.17032v3-abstract-full" style="display: none;"> Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment and algorithm implementations. This makes it difficult for researchers to compare and build upon each other's work, slowing down progress in the field. Gymnasium is an open-source library that provides a standard API for RL environments, aiming to tackle this issue. Gymnasium's main feature is a set of abstractions that allow for wide interoperability between environments and training algorithms, making it easier for researchers to develop and test RL algorithms. In addition, Gymnasium provides a collection of easy-to-use environments, tools for easily customizing environments, and tools to ensure the reproducibility and robustness of RL research. Through this unified framework, Gymnasium significantly streamlines the process of developing and testing RL algorithms, enabling researchers to focus more on innovation and less on implementation details. By providing a standardized platform for RL research, Gymnasium helps to drive forward the field of reinforcement learning and unlock its full potential. Gymnasium is available online at https://github.com/Farama-Foundation/Gymnasium <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.17032v3-abstract-full').style.display = 'none'; document.getElementById('2407.17032v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.03046">arXiv:2402.03046</a> <span> [<a href="https://arxiv.org/pdf/2402.03046">pdf</a>, <a href="https://arxiv.org/format/2402.03046">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huang%2C+S">Shengyi Huang</a>, <a href="/search/cs?searchtype=author&query=Gallou%C3%A9dec%2C+Q">Quentin Gallou茅dec</a>, <a href="/search/cs?searchtype=author&query=Felten%2C+F">Florian Felten</a>, <a href="/search/cs?searchtype=author&query=Raffin%2C+A">Antonin Raffin</a>, <a href="/search/cs?searchtype=author&query=Dossa%2C+R+F+J">Rousslan Fernand Julien Dossa</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+Y">Yanxiao Zhao</a>, <a href="/search/cs?searchtype=author&query=Sullivan%2C+R">Ryan Sullivan</a>, <a href="/search/cs?searchtype=author&query=Makoviychuk%2C+V">Viktor Makoviychuk</a>, <a href="/search/cs?searchtype=author&query=Makoviichuk%2C+D">Denys Makoviichuk</a>, <a href="/search/cs?searchtype=author&query=Danesh%2C+M+H">Mohamad H. Danesh</a>, <a href="/search/cs?searchtype=author&query=Roum%C3%A9gous%2C+C">Cyril Roum茅gous</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+J">Jiayi Weng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+C">Chufan Chen</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+M+M">Md Masudur Rahman</a>, <a href="/search/cs?searchtype=author&query=Ara%C3%BAjo%2C+J+G+M">Jo茫o G. M. Ara煤jo</a>, <a href="/search/cs?searchtype=author&query=Quan%2C+G">Guorui Quan</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+D">Daniel Tan</a>, <a href="/search/cs?searchtype=author&query=Klein%2C+T">Timo Klein</a>, <a href="/search/cs?searchtype=author&query=Charakorn%2C+R">Rujikorn Charakorn</a>, <a href="/search/cs?searchtype=author&query=Towers%2C+M">Mark Towers</a>, <a href="/search/cs?searchtype=author&query=Berthelot%2C+Y">Yann Berthelot</a>, <a href="/search/cs?searchtype=author&query=Mehta%2C+K">Kinal Mehta</a>, <a href="/search/cs?searchtype=author&query=Chakraborty%2C+D">Dipam Chakraborty</a>, <a href="/search/cs?searchtype=author&query=KG%2C+A">Arjun KG</a>, <a href="/search/cs?searchtype=author&query=Charraut%2C+V">Valentin Charraut</a> , et al. (8 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.03046v1-abstract-short" style="display: inline;"> In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone. We present Open RL Benchmark, a set of fully tracked RL experiments, i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.03046v1-abstract-full').style.display = 'inline'; document.getElementById('2402.03046v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.03046v1-abstract-full" style="display: none;"> In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone. We present Open RL Benchmark, a set of fully tracked RL experiments, including not only the usual data such as episodic return, but also all algorithm-specific and system metrics. Open RL Benchmark is community-driven: anyone can download, use, and contribute to the data. At the time of writing, more than 25,000 runs have been tracked, for a cumulative duration of more than 8 years. Open RL Benchmark covers a wide range of RL libraries and reference implementations. Special care is taken to ensure that each experiment is precisely reproducible by providing not only the full parameters, but also the versions of the dependencies used to generate it. In addition, Open RL Benchmark comes with a command-line interface (CLI) for easy fetching and generating figures to present the results. In this document, we include two case studies to demonstrate the usefulness of Open RL Benchmark in practice. To the best of our knowledge, Open RL Benchmark is the first RL benchmark of its kind, and the authors hope that it will improve and facilitate the work of researchers in the field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.03046v1-abstract-full').style.display = 'none'; document.getElementById('2402.03046v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.13831">arXiv:2306.13831</a> <span> [<a href="https://arxiv.org/pdf/2306.13831">pdf</a>, <a href="https://arxiv.org/format/2306.13831">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chevalier-Boisvert%2C+M">Maxime Chevalier-Boisvert</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+B">Bolun Dai</a>, <a href="/search/cs?searchtype=author&query=Towers%2C+M">Mark Towers</a>, <a href="/search/cs?searchtype=author&query=de+Lazcano%2C+R">Rodrigo de Lazcano</a>, <a href="/search/cs?searchtype=author&query=Willems%2C+L">Lucas Willems</a>, <a href="/search/cs?searchtype=author&query=Lahlou%2C+S">Salem Lahlou</a>, <a href="/search/cs?searchtype=author&query=Pal%2C+S">Suman Pal</a>, <a href="/search/cs?searchtype=author&query=Castro%2C+P+S">Pablo Samuel Castro</a>, <a href="/search/cs?searchtype=author&query=Terry%2C+J">Jordan Terry</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.13831v1-abstract-short" style="display: inline;"> We present the Minigrid and Miniworld libraries which provide a suite of goal-oriented 2D and 3D environments. The libraries were explicitly created with a minimalistic design paradigm to allow users to rapidly develop new environments for a wide range of research-specific needs. As a result, both have received widescale adoption by the RL community, facilitating research in a wide range of areas.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.13831v1-abstract-full').style.display = 'inline'; document.getElementById('2306.13831v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.13831v1-abstract-full" style="display: none;"> We present the Minigrid and Miniworld libraries which provide a suite of goal-oriented 2D and 3D environments. The libraries were explicitly created with a minimalistic design paradigm to allow users to rapidly develop new environments for a wide range of research-specific needs. As a result, both have received widescale adoption by the RL community, facilitating research in a wide range of areas. In this paper, we outline the design philosophy, environment details, and their world generation API. We also showcase the additional capabilities brought by the unified API between Minigrid and Miniworld through case studies on transfer learning (for both RL agents and humans) between the different observation spaces. The source code of Minigrid and Miniworld can be found at https://github.com/Farama-Foundation/{Minigrid, Miniworld} along with their documentation at https://{minigrid, miniworld}.farama.org/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.13831v1-abstract-full').style.display = 'none'; document.getElementById('2306.13831v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.10402">arXiv:2003.10402</a> <span> [<a href="https://arxiv.org/pdf/2003.10402">pdf</a>, <a href="https://arxiv.org/format/2003.10402">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> DevSecOps in Robotics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mayoral-Vilches%2C+V">V铆ctor Mayoral-Vilches</a>, <a href="/search/cs?searchtype=author&query=Garc%C3%ADa-Maestro%2C+N">Nuria Garc铆a-Maestro</a>, <a href="/search/cs?searchtype=author&query=Towers%2C+M">McKenna Towers</a>, <a href="/search/cs?searchtype=author&query=Gil-Uriarte%2C+E">Endika Gil-Uriarte</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.10402v3-abstract-short" style="display: inline;"> Quality in software is often understood as "execution according to design purpose" whereas security means that "software will not put data or computing systems at risk of unauthorized access." There seems to be a connection between these two aspects but, how do we integrate both of them in the robotics development cycle? In this article we introduce DevSecOps in Robotics, a set of best practices d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.10402v3-abstract-full').style.display = 'inline'; document.getElementById('2003.10402v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.10402v3-abstract-full" style="display: none;"> Quality in software is often understood as "execution according to design purpose" whereas security means that "software will not put data or computing systems at risk of unauthorized access." There seems to be a connection between these two aspects but, how do we integrate both of them in the robotics development cycle? In this article we introduce DevSecOps in Robotics, a set of best practices designed to help roboticists implant security deep in the heart of their development and operations processes. First, we briefly describe DevOps, introduce the value added with DevSecOps and describe and illustrate how these practices may be implemented in the robotics field. We finalize with a discussion on the relationship between security, quality and safety, open problems and future research questions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.10402v3-abstract-full').style.display = 'none'; document.getElementById('2003.10402v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div 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