Summary of Game On: Towards Language Models As Rl Experimenters, by Jingwei Zhang et al.
Game On: Towards Language Models as RL Experimenters
by Jingwei Zhang, Thomas Lampe, Abbas Abdolmaleki, Jost Tobias Springenberg, Martin Riedmiller
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 agent architecture automates parts of the reinforcement learning experiment workflow to enable automated mastery of control domains for embodied agents. The system leverages a Visual Language Model (VLM) to perform tasks such as monitoring experiment progress, proposing new tasks based on past successes and failures, decomposing tasks into subtasks (skills), and retrieving skills to execute. This enables the system to build automated curricula for learning. A prototype of this system is provided, and current models and techniques are examined for their feasibility in achieving the desired level of automation. The system uses a standard Gemini model without fine-tuning to provide a curriculum of skills to a language-conditioned Actor-Critic algorithm, steering data collection to aid learning new skills. Data collected shows promise for learning and improving control policies in robotics domains. The architecture also demonstrates potential for building a growing library of skills and judging the progress of training those skills. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an AI system that helps other AI systems learn new tasks by automating parts of the process. This system uses a special kind of AI model called a Visual Language Model to make decisions and provide guidance. It can help create customized learning plans for each AI system, making it easier for them to learn new skills. The researchers tested this system with a simple AI algorithm and found that it was effective in helping the algorithm learn new tasks. |
Keywords
» Artificial intelligence » Fine tuning » Gemini » Language model » Reinforcement learning
