Design and training of deep reinforcement learning agents von Pardo, Fabio

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Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking von Pardo, Fabio

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Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking von Pardo, Fabio

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LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations von Ruoss, Anian, Pardo, Fabio, Chan, Harris, Li, Bonnie, Mnih, Volodymyr, Genewein, Tim

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Ivy: Templated Deep Learning for Inter-Framework Portability von Lenton, Daniel, Pardo, Fabio, Falck, Fabian, James, Stephen, Clark, Ronald

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Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks von Pardo, Fabio, Levdik, Vitaly, Kormushev, Petar

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OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion von La Barbera, Vittorio, Pardo, Fabio, Tassa, Yuval, Daley, Monica, Richards, Christopher, Kormushev, Petar, Hutchinson, John

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Goal-oriented Trajectories for Efficient Exploration von Pardo, Fabio, Levdik, Vitaly, Kormushev, Petar

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Action Branching Architectures for Deep Reinforcement Learning von Tavakoli, Arash, Pardo, Fabio, Kormushev, Petar

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LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations von Ruoss, Anian, Pardo, Fabio, Harris, Chan, Li, Bonnie, Mnih, Volodymyr, Genewein, Tim

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Vision-Language Models as a Source of Rewards von Baumli, Kate, Baveja, Satinder, Behbahani, Feryal, Chan, Harris, Comanici, Gheorghe, Flennerhag, Sebastian, Gazeau, Maxime, Holsheimer, Kristian, Horgan, Dan, Laskin, Michael, Lyle, Clare, Masoom, Hussain, McKinney, Kay, Mnih, Volodymyr, Neitz, Alexander, Nikulin, Dmitry, Pardo, Fabio, Parker-Holder, Jack, Quan, John, Rocktäschel, Tim, Sahni, Himanshu, Schaul, Tom, Schroecker, Yannick, Spencer, Stephen, Steigerwald, Richie, Wang, Luyu, Zhang, Lei

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QUALIDADE FISIOLÓGICA DE SEMENTES DE SOJA ESVERDEADAS EM DIFERENTES TAMANHOS von Pardo, Fábio Faustini, Binotti, Flávio Ferreira da Silva, Cardoso, Eliana Duarte, Costa, Edilson

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Time Limits in Reinforcement Learning von Pardo, Fabio, Tavakoli, Arash, Levdik, Vitaly, Kormushev, Petar

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Time Limits in Reinforcement Learning von Pardo, Fabio, Tavakoli, Arash, Levdik, Vitaly, Kormushev, Petar

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Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks von Pardo, Fabio, Levdik, Vitaly, Kormushev, Petar

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Ivy: Templated Deep Learning for Inter-Framework Portability von Lenton, Daniel, Pardo, Fabio, Falck, Fabian, James, Stephen, Clark, Ronald

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Action Branching Architectures for Deep Reinforcement Learning von Tavakoli, Arash, Pardo, Fabio, Kormushev, Petar

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OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion von Vittorio La Barbera, Pardo, Fabio, Tassa, Yuval, Daley, Monica, Richards, Christopher, Kormushev, Petar, Hutchinson, John

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Goal-oriented Trajectories for Efficient Exploration von Pardo, Fabio, Levdik, Vitaly, Kormushev, Petar

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Vision-Language Models as a Source of Rewards von Baumli, Kate, Baveja, Satinder, Behbahani, Feryal, Harris, Chan, Comanici, Gheorghe, Flennerhag, Sebastian, Gazeau, Maxime, Holsheimer, Kristian, Horgan, Dan, Laskin, Michael, Lyle, Clare, Masoom, Hussain, McKinney, Kay, Mnih, Volodymyr, Neitz, Alexander, Nikulin, Dmitry, Pardo, Fabio, Parker-Holder, Jack, Quan, John, Rocktäschel, Tim, Sahni, Himanshu, Schaul, Tom, Schroecker, Yannick, Spencer, Stephen, Steigerwald, Richie, Wang, Luyu, Zhang, Lei

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