Summary of Envgen: Generating and Adapting Environments Via Llms For Training Embodied Agents, by Abhay Zala et al.
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents
by Abhay Zala, Jaemin Cho, Han Lin, Jaehong Yoon, Mohit Bansal
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper introduces EnvGen, a novel framework that utilizes large language models (LLMs) to adaptively create training environments for smaller reinforcement learning (RL) agents. The authors demonstrate the effectiveness of EnvGen by training a small RL agent in a mixture of original and LLM-generated environments, which outperforms SOTA methods including a GPT-4 agent and learns long-horizon tasks significantly faster. Additionally, EnvGen is more efficient, requiring only a small number of LLM calls compared to directly employing LLMs as agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how AI can help robots learn new skills by generating training environments that are specifically designed for them. The researchers use large language models to create different scenarios and adjust the difficulty level based on the robot’s performance. This approach is better than using a single, fixed environment because it allows the robot to learn at its own pace. The results show that this method can help robots learn faster and more efficiently than before. |
Keywords
* Artificial intelligence * Gpt * Reinforcement learning
