Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Dasagi, Vibhavari"'
Autor:
Gemp, Ian, Bachrach, Yoram, Lanctot, Marc, Patel, Roma, Dasagi, Vibhavari, Marris, Luke, Piliouras, Georgios, Liu, Siqi, Tuyls, Karl
Game theory is the study of mathematical models of strategic interactions among rational agents. Language is a key medium of interaction for humans, though it has historically proven difficult to model dialogue and its strategic motivations mathemati
Externí odkaz:
http://arxiv.org/abs/2402.01704
Autor:
Adaptive Agent Team, Bauer, Jakob, Baumli, Kate, Baveja, Satinder, Behbahani, Feryal, Bhoopchand, Avishkar, Bradley-Schmieg, Nathalie, Chang, Michael, Clay, Natalie, Collister, Adrian, Dasagi, Vibhavari, Gonzalez, Lucy, Gregor, Karol, Hughes, Edward, Kashem, Sheleem, Loks-Thompson, Maria, Openshaw, Hannah, Parker-Holder, Jack, Pathak, Shreya, Perez-Nieves, Nicolas, Rakicevic, Nemanja, Rocktäschel, Tim, Schroecker, Yannick, Sygnowski, Jakub, Tuyls, Karl, York, Sarah, Zacherl, Alexander, Zhang, Lei
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL). In this work, we demonstrate that training an
Externí odkaz:
http://arxiv.org/abs/2301.07608
Autor:
Gemp, Ian, Anthony, Thomas, Bachrach, Yoram, Bhoopchand, Avishkar, Bullard, Kalesha, Connor, Jerome, Dasagi, Vibhavari, De Vylder, Bart, Duenez-Guzman, Edgar, Elie, Romuald, Everett, Richard, Hennes, Daniel, Hughes, Edward, Khan, Mina, Lanctot, Marc, Larson, Kate, Lever, Guy, Liu, Siqi, Marris, Luke, McKee, Kevin R., Muller, Paul, Perolat, Julien, Strub, Florian, Tacchetti, Andrea, Tarassov, Eugene, Wang, Zhe, Tuyls, Karl
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d hu
Externí odkaz:
http://arxiv.org/abs/2209.10958
Autor:
Lambert, Nathan, Wulfmeier, Markus, Whitney, William, Byravan, Arunkumar, Bloesch, Michael, Dasagi, Vibhavari, Hertweck, Tim, Riedmiller, Martin
Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal behaviour. The second step has been widely studied in the offline sett
Externí odkaz:
http://arxiv.org/abs/2201.11861
Autor:
Rana, Krishan, Dasagi, Vibhavari, Haviland, Jesse, Talbot, Ben, Milford, MIchael, Sünderhauf, Niko
While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the real world due to mismatches between the training and execution environ
Externí odkaz:
http://arxiv.org/abs/2112.05299
Autor:
Groth, Oliver, Wulfmeier, Markus, Vezzani, Giulia, Dasagi, Vibhavari, Hertweck, Tim, Hafner, Roland, Heess, Nicolas, Riedmiller, Martin
Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks. However, as the agent learns to reach previously unexplored spaces and the objective ada
Externí odkaz:
http://arxiv.org/abs/2109.08603
Autor:
Rana, Krishan, Dasagi, Vibhavari, Haviland, Jesse, Talbot, Ben, Milford, Michael, Sünderhauf, Niko
We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learning (RL). BCF thrives in the robotics domain, where reliable but subopti
Externí odkaz:
http://arxiv.org/abs/2107.09822
Manipulating deformable objects, such as fabric, is a long standing problem in robotics, with state estimation and control posing a significant challenge for traditional methods. In this paper, we show that it is possible to learn fabric folding skil
Externí odkaz:
http://arxiv.org/abs/2010.03209
Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is
Externí odkaz:
http://arxiv.org/abs/2003.05117
Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task. Robotics is a significant potential application domain for many of these a
Externí odkaz:
http://arxiv.org/abs/1911.08666