1 code implementation • 14 Mar 2024 • Zohar Rimon, Tom Jurgenson, Orr Krupnik, Gilad Adler, Aviv Tamar
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration.
1 code implementation • 19 Oct 2023 • Orr Krupnik, Elisei Shafer, Tom Jurgenson, Aviv Tamar
Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions.
no code implementations • 29 Jan 2019 • Orr Krupnik, Igor Mordatch, Aviv Tamar
We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems -- an important domain for robots interacting with other agents in the same workspace.