Search Results - "Toyama, Daniel"

Not All LLM Reasoners Are Created Equal by Hosseini, Arian, Sordoni, Alessandro, Toyama, Daniel, Courville, Aaron, Agarwal, Rishabh

“…We study the depth of grade-school math (GSM) problem-solving capabilities of LLMs. To this end, we evaluate their performance on pairs of existing math word…”
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Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning by Comanici, Gheorghe, Glaese, Amelia, Gergely, Anita, Toyama, Daniel, Ahmed, Zafarali, Jackson, Tyler, Hamel, Philippe, Precup, Doina

“…Hierarchical Reinforcement Learning (HRL) allows interactive agents to decompose complex problems into a hierarchy of sub-tasks. Higher-level tasks can invoke…”
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AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents by Rawles, Christopher, Clinckemaillie, Sarah, Chang, Yifan, Waltz, Jonathan, Lau, Gabrielle, Fair, Marybeth, Li, Alice, Bishop, William, Li, Wei, Campbell-Ajala, Folawiyo, Toyama, Daniel, Berry, Robert, Tyamagundlu, Divya, Lillicrap, Timothy, Riva, Oriana

“…Autonomous agents that execute human tasks by controlling computers can enhance human productivity and application accessibility. However, progress in this…”
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Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search by Mehrabian, Abbas, Anand, Ankit, Kim, Hyunjik, Sonnerat, Nicolas, Balog, Matej, Comanici, Gheorghe, Berariu, Tudor, Lee, Andrew, Ruoss, Anian, Bulanova, Anna, Toyama, Daniel, Blackwell, Sam, Paredes, Bernardino Romera, Veličković, Petar, Orseau, Laurent, Lee, Joonkyung, Naredla, Anurag Murty, Precup, Doina, Wagner, Adam Zsolt

“…This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of…”
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AndroidEnv: A Reinforcement Learning Platform for Android by Toyama, Daniel, Hamel, Philippe, Gergely, Anita, Comanici, Gheorghe, Glaese, Amelia, Ahmed, Zafarali, Jackson, Tyler, Mourad, Shibl, Precup, Doina

“…We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv allows RL agents to…”
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RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning by Ramos, Sabela, Girgin, Sertan, Hussenot, Léonard, Vincent, Damien, Yakubovich, Hanna, Toyama, Daniel, Gergely, Anita, Stanczyk, Piotr, Marinier, Raphael, Harmsen, Jeremiah, Pietquin, Olivier, Momchev, Nikola

“…We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of…”
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AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning by Mathieu, Michaël, Ozair, Sherjil, Srinivasan, Srivatsan, Gulcehre, Caglar, Zhang, Shangtong, Jiang, Ray, Paine, Tom Le, Powell, Richard, Żołna, Konrad, Schrittwieser, Julian, Choi, David, Georgiev, Petko, Toyama, Daniel, Huang, Aja, Ring, Roman, Babuschkin, Igor, Ewalds, Timo, Bordbar, Mahyar, Henderson, Sarah, Colmenarejo, Sergio Gómez, Oord, Aäron van den, Czarnecki, Wojciech Marian, de Freitas, Nando, Vinyals, Oriol

“…StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering…”
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The Option Keyboard: Combining Skills in Reinforcement Learning by Barreto, André, Borsa, Diana, Hou, Shaobo, Comanici, Gheorghe, Aygün, Eser, Hamel, Philippe, Toyama, Daniel, Hunt, Jonathan, Mourad, Shibl, Silver, David, Precup, Doina

“…The ability to combine known skills to create new ones may be crucial in the solution of complex reinforcement learning problems that unfold over extended…”
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Scaling Language Models: Methods, Analysis & Insights from Training Gopher by Rae, Jack W, Borgeaud, Sebastian, Cai, Trevor, Millican, Katie, Hoffmann, Jordan, Song, Francis, Aslanides, John, Henderson, Sarah, Ring, Roman, Young, Susannah, Rutherford, Eliza, Hennigan, Tom, Menick, Jacob, Cassirer, Albin, Powell, Richard, Driessche, George van den, Hendricks, Lisa Anne, Rauh, Maribeth, Huang, Po-Sen, Glaese, Amelia, Welbl, Johannes, Dathathri, Sumanth, Huang, Saffron, Uesato, Jonathan, Mellor, John, Higgins, Irina, Creswell, Antonia, McAleese, Nat, Wu, Amy, Elsen, Erich, Jayakumar, Siddhant, Buchatskaya, Elena, Budden, David, Sutherland, Esme, Simonyan, Karen, Paganini, Michela, Sifre, Laurent, Martens, Lena, Li, Xiang Lorraine, Kuncoro, Adhiguna, Nematzadeh, Aida, Gribovskaya, Elena, Donato, Domenic, Lazaridou, Angeliki, Mensch, Arthur, Lespiau, Jean-Baptiste, Tsimpoukelli, Maria, Grigorev, Nikolai, Fritz, Doug, Sottiaux, Thibault, Pajarskas, Mantas, Pohlen, Toby, Gong, Zhitao, Toyama, Daniel, d'Autume, Cyprien de Masson, Li, Yujia, Terzi, Tayfun, Mikulik, Vladimir, Babuschkin, Igor, Clark, Aidan, Casas, Diego de Las, Guy, Aurelia, Jones, Chris, Bradbury, James, Johnson, Matthew, Hechtman, Blake, Weidinger, Laura, Gabriel, Iason, Isaac, William, Lockhart, Ed, Osindero, Simon, Rimell, Laura, Dyer, Chris, Vinyals, Oriol, Ayoub, Kareem, Stanway, Jeff, Bennett, Lorrayne, Hassabis, Demis, Kavukcuoglu, Koray, Irving, Geoffrey

“…Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and…”
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