1 code implementation • 16 Feb 2024 • Moritz Stephan, Alexander Khazatsky, Eric Mitchell, Annie S Chen, Sheryl Hsu, Archit Sharma, Chelsea Finn
The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements and preferences.
2 code implementations • 26 Jan 2023 • Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, Chelsea Finn
In this paper, we identify a property of the structure of an LLM's probability function that is useful for such detection.
1 code implementation • 6 Oct 2021 • Benjamin Eysenbach, Alexander Khazatsky, Sergey Levine, Ruslan Salakhutdinov
Many model-based reinforcement learning (RL) methods follow a similar template: fit a model to previously observed data, and then use data from that model for RL or planning.
Model-based Reinforcement Learning Reinforcement Learning (RL)
2 code implementations • 1 Jun 2021 • Alexander Khazatsky, Ashvin Nair, Daniel Jing, Sergey Levine
In effect, prior data is used to learn what kinds of outcomes may be possible, such that when the robot encounters an unfamiliar setting, it can sample potential outcomes from its model, attempt to reach them, and thereby update both its skills and its outcome model.
no code implementations • 23 Apr 2021 • Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair, Alexander Khazatsky, Glen Berseth, Sergey Levine
Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity.
1 code implementation • 23 Oct 2019 • Ashvin Nair, Shikhar Bahl, Alexander Khazatsky, Vitchyr Pong, Glen Berseth, Sergey Levine
When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand.