RL Zero: Zero-Shot Language to Behaviors without any Supervision
Authors: name: Harshit Sikchi, name: Siddhant Agarwal, name: Pranaya Jajoo, name: Samyak Parajuli, name: Caleb Chuck, name: Max Rudolph, name: Peter Stone, name: Amy Zhang, name: Scott Niekum

Rewards remain an uninterpretable way to specify tasks for Reinforcement
Learning, as humans are often unable to predict the optimal behavior of any
given reward function, leading to poor reward design and reward hacking.
Language presents an appealing way to communicate intent to agents and bypass
reward design, but prior efforts to do so have been limited by costly and
unscalable labeling efforts. In this work, we propose a method for a completely
unsupervised alternative to grounding language instructions in a zero-shot
manner to obtain policies. We present a solution that takes the form of
imagine, project, and imitate: The agent imagines the observation sequence
corresponding to the language description of a task, projects the imagined
sequence to our target domain, and grounds it to a policy. Video-language
models allow us to imagine task descriptions that leverage knowledge of tasks
learned from internet-scale video-text mappings. The challenge remains to
ground these generations to a policy. In this work, we show that we can achieve
a zero-shot language-to-behavior policy by first grounding the imagined
sequences in real observations of an unsupervised RL agent and using a
closed-form solution to imitation learning that allows the RL agent to mimic
the grounded observations. Our method, RLZero, is the first to our knowledge to
show zero-shot language to behavior generation abilities without any
supervision on a variety of tasks on simulated domains. We further show that
RLZero can also generate policies zero-shot from cross-embodied videos such as
those scraped from YouTube.