Summary of Rl Zero: Zero-shot Language to Behaviors Without Any Supervision, by Harshit Sikchi et al.
RL Zero: Zero-Shot Language to Behaviors without any Supervision
by Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo, Samyak Parajuli, Caleb Chuck, Max Rudolph, Peter Stone, Amy Zhang, Scott Niekum
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method in this paper presents a novel approach to specifying tasks for Reinforcement Learning (RL) without requiring human designers to predict the optimal behavior of reward functions. This is achieved through a completely unsupervised alternative that grounds language instructions in a zero-shot manner to obtain policies. The method, called RLZero, consists of three steps: imagining the observation sequence corresponding to a language description, projecting it to the target domain, and grounding it to a policy. The authors leverage video-language models to generate task descriptions that leverage knowledge of tasks learned from internet-scale video-text mappings. The paper demonstrates the effectiveness of RLZero in achieving zero-shot language-to-behavior policy generation without supervision on various simulated domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to help computers learn from language instructions, without needing humans to design rewards or labels. The approach is called RLZero and it works by imagining what an agent would observe if it followed the language instruction, then using that observation to generate a policy. This method doesn’t require any supervision or human intervention, making it a significant breakthrough in the field of Reinforcement Learning. |
Keywords
» Artificial intelligence » Grounding » Reinforcement learning » Unsupervised » Zero shot
