Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Wulfmeier, Markus"'
Autor:
Wulfmeier, Markus, Bloesch, Michael, Vieillard, Nino, Ahuja, Arun, Bornschein, Jorg, Huang, Sandy, Sokolov, Artem, Barnes, Matt, Desjardins, Guillaume, Bewley, Alex, Bechtle, Sarah Maria Elisabeth, Springenberg, Jost Tobias, Momchev, Nikola, Bachem, Olivier, Geist, Matthieu, Riedmiller, Martin
The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback (RLHF). The simplicity and scalability of maximum
Externí odkaz:
http://arxiv.org/abs/2409.01369
Autor:
Tirumala, Dhruva, Wulfmeier, Markus, Moran, Ben, Huang, Sandy, Humplik, Jan, Lever, Guy, Haarnoja, Tuomas, Hasenclever, Leonard, Byravan, Arunkumar, Batchelor, Nathan, Sreendra, Neil, Patel, Kushal, Gwira, Marlon, Nori, Francesco, Riedmiller, Martin, Heess, Nicolas
We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting reflects many challenges of real-world robotics, including active perce
Externí odkaz:
http://arxiv.org/abs/2405.02425
Recent reinforcement learning approaches have shown surprisingly strong capabilities of bang-bang policies for solving continuous control benchmarks. The underlying coarse action space discretizations often yield favourable exploration characteristic
Externí odkaz:
http://arxiv.org/abs/2404.04253
Autor:
Bhardwaj, Mohak, Lampe, Thomas, Neunert, Michael, Romano, Francesco, Abdolmaleki, Abbas, Byravan, Arunkumar, Wulfmeier, Markus, Riedmiller, Martin, Buchli, Jonas
Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale. However, domains such as fluid dynamical systems exhibit complex dynamic phenomena that are hard to simulate
Externí odkaz:
http://arxiv.org/abs/2402.06102
Autor:
Lampe, Thomas, Abdolmaleki, Abbas, Bechtle, Sarah, Huang, Sandy H., Springenberg, Jost Tobias, Bloesch, Michael, Groth, Oliver, Hafner, Roland, Hertweck, Tim, Neunert, Michael, Wulfmeier, Markus, Zhang, Jingwei, Nori, Francesco, Heess, Nicolas, Riedmiller, Martin
Reinforcement learning solely from an agent's self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed. However, if done right, agents learning from real data can be surprisingly efficient t
Externí odkaz:
http://arxiv.org/abs/2312.11374
Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by the growth of required resources, expansive datasets and corresponding investments into computing infrastructure. Although earlier successes predominantly f
Externí odkaz:
http://arxiv.org/abs/2312.01939
Autor:
Pinneri, Cristina, Bechtle, Sarah, Wulfmeier, Markus, Byravan, Arunkumar, Zhang, Jingwei, Whitney, William F., Riedmiller, Martin
We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment. Specifically, we aim to improve the ag
Externí odkaz:
http://arxiv.org/abs/2309.07578
Autor:
Gürtler, Nico, Widmaier, Felix, Sancaktar, Cansu, Blaes, Sebastian, Kolev, Pavel, Bauer, Stefan, Wüthrich, Manuel, Wulfmeier, Markus, Riedmiller, Martin, Allshire, Arthur, Wang, Qiang, McCarthy, Robert, Kim, Hangyeol, Baek, Jongchan, Kwon, Wookyong, Qian, Shanliang, Toshimitsu, Yasunori, Michelis, Mike Yan, Kazemipour, Amirhossein, Raayatsanati, Arman, Zheng, Hehui, Cangan, Barnabas Gavin, Schölkopf, Bernhard, Martius, Georg
Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms. However, insights gained in simulation do not nece
Externí odkaz:
http://arxiv.org/abs/2308.07741
Autor:
Di Palo, Norman, Byravan, Arunkumar, Hasenclever, Leonard, Wulfmeier, Markus, Heess, Nicolas, Riedmiller, Martin
Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and planning-like behaviour, in text form, among many others. In this work, w
Externí odkaz:
http://arxiv.org/abs/2307.09668
Autor:
Barnes, Matt, Abueg, Matthew, Lange, Oliver F., Deeds, Matt, Trader, Jason, Molitor, Denali, Wulfmeier, Markus, O'Banion, Shawn
Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addressed planetary-scale problems with hundreds of millions of states and
Externí odkaz:
http://arxiv.org/abs/2305.11290