Zobrazeno 1 - 10
of 11 704
pro vyhledávání: '"A, LITTMAN"'
Autor:
Allen, Cameron, Kirtland, Aaron, Tao, Ruo Yu, Lobel, Sam, Scott, Daniel, Petrocelli, Nicholas, Gottesman, Omer, Parr, Ronald, Littman, Michael L., Konidaris, George
Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can a
Externí odkaz:
http://arxiv.org/abs/2407.07333
Many recent works have explored using language models for planning problems. One line of research focuses on translating natural language descriptions of planning tasks into structured planning languages, such as the planning domain definition langua
Externí odkaz:
http://arxiv.org/abs/2407.03321
Autor:
Shani, Lior, Lueb, Pim, Menning, Gavin, Gupta, Mohit, Riggert, Colin, Littman, Tyler, Hackbarth, Frey, Rossi, Marco, Jung, Jason, Badawy, Ghada, Verheijen, Marcel A., Crowell, Paul, Bakkers, Erik P. A. M., Pribiag, Vlad S.
Quantum devices based on InSb nanowires (NWs) are a prime candidate system for realizing and exploring topologically-protected quantum states and for electrically-controlled spin-based qubits. The influence of disorder on achieving reliable topologic
Externí odkaz:
http://arxiv.org/abs/2306.00117
Autor:
Abate, Marcus, Schwartz, Ariel, Wong, Xue Iuan, Luo, Wangdong, Littman, Rotem, Klinger, Marc, Kuhnert, Lars, Blue, Douglas, Carlone, Luca
Localization and mapping are key capabilities for self-driving vehicles. In this paper, we build on Kimera and extend it to use multiple cameras as well as external (eg wheel) odometry sensors, to obtain accurate and robust odometry estimates in real
Externí odkaz:
http://arxiv.org/abs/2304.13182
In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal policy with high probability using a finite amount of samples and compu
Externí odkaz:
http://arxiv.org/abs/2303.05518
Autor:
Baker, Megan M., New, Alexander, Aguilar-Simon, Mario, Al-Halah, Ziad, Arnold, Sébastien M. R., Ben-Iwhiwhu, Ese, Brna, Andrew P., Brooks, Ethan, Brown, Ryan C., Daniels, Zachary, Daram, Anurag, Delattre, Fabien, Dellana, Ryan, Eaton, Eric, Fu, Haotian, Grauman, Kristen, Hostetler, Jesse, Iqbal, Shariq, Kent, Cassandra, Ketz, Nicholas, Kolouri, Soheil, Konidaris, George, Kudithipudi, Dhireesha, Learned-Miller, Erik, Lee, Seungwon, Littman, Michael L., Madireddy, Sandeep, Mendez, Jorge A., Nguyen, Eric Q., Piatko, Christine D., Pilly, Praveen K., Raghavan, Aswin, Rahman, Abrar, Ramakrishnan, Santhosh Kumar, Ratzlaff, Neale, Soltoggio, Andrea, Stone, Peter, Sur, Indranil, Tang, Zhipeng, Tiwari, Saket, Vedder, Kyle, Wang, Felix, Xu, Zifan, Yanguas-Gil, Angel, Yedidsion, Harel, Yu, Shangqun, Vallabha, Gautam K.
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original t
Externí odkaz:
http://arxiv.org/abs/2301.07799
Autor:
Alyson J Littman, Andrew K Timmons, Anna Korpak, Kwun C G Chan, Kenneth T Jones, Suzanne Shirley, Kyle Nordrum, Jeffrey Robbins, Suhail Masadeh, Ernest Moy
Publikováno v:
JMIR Diabetes, Vol 9, Pp e53083-e53083 (2024)
Abstract BackgroundIn-home remote foot temperature monitoring (RTM) holds promise as a method to reduce foot ulceration in high-risk patients with diabetes. Few studies have evaluated adherence to this method or evaluated the factors associated with
Externí odkaz:
https://doaj.org/article/d4f406d4ad184cc9874d594a2965d2f0
Publikováno v:
Reinforcement Learning Journal, vol. 1, no. 1, 2024, pp. TBD
Reinforcement-learning agents seek to maximize a reward signal through environmental interactions. As humans, our job in the learning process is to design reward functions to express desired behavior and enable the agent to learn such behavior swiftl
Externí odkaz:
http://arxiv.org/abs/2212.03733
Autor:
Lovering, Charles, Forde, Jessica Zosa, Konidaris, George, Pavlick, Ellie, Littman, Michael L.
AlphaZero, an approach to reinforcement learning that couples neural networks and Monte Carlo tree search (MCTS), has produced state-of-the-art strategies for traditional board games like chess, Go, shogi, and Hex. While researchers and game commenta
Externí odkaz:
http://arxiv.org/abs/2211.14673
Recent advances in reinforcement-learning research have demonstrated impressive results in building algorithms that can out-perform humans in complex tasks. Nevertheless, creating reinforcement-learning systems that can build abstractions of their ex
Externí odkaz:
http://arxiv.org/abs/2211.03281