We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning.

We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.

All the code and data are open-sourced at our website.

Domain transfer methods aim to bridge this dynamics gap through techniques such as domain adaptation or domain calibration.

Both challenges involve modeling long-term dependencies.