EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents

แนวคิดหลัก

LLMs can adaptively generate training environments to help smaller RL agents learn multiple skills efficiently.

บทคัดย่อ

EnvGen proposes a novel framework where LLMs generate training environments to improve RL agent performance. The method involves generating custom environments, training the agent in these environments, measuring performance, and providing feedback to adapt the environments. Comprehensive experiments in Crafter and Heist game environments demonstrate EnvGen's effectiveness in improving agent performance on long-horizon tasks. The framework is efficient, requiring minimal LLM calls compared to existing methods.

Recent approaches use LLMs for embodied learning.
EnvGen aims to create adaptive training environments.
Experiments show improved RL agent performance with EnvGen.
Efficient method with minimal LLM calls.

สถิติ

LLMエージェントは、1エピソードあたり数千回のLLM呼び出しを必要とする（SPRING (Wu et al., 2023)）。
EnvGenは、合計4回のLLM呼び出しで効果的なトレーニング環境を生成する。

คำพูด

"Instead of directly employing LLMs as embodied agents, can we use LLMs’ reasoning capability to adaptively create training environments to help smaller embodied RL agents learn useful skills that they are weak at?"
"We find that a small RL agent trained with EnvGen can outperform SOTA methods, including a GPT-4 agent, and learns long-horizon tasks significantly faster."

ข้อมูลเชิงลึกที่สำคัญจาก

EnvGen

by Abhay Zala,J... ที่ arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.12014.pdf

สอบถามเพิ่มเติม

How does EnvGen compare to other methods using frequent LLM calls

EnvGen stands out from other methods that rely on frequent LLM calls by significantly reducing the number of times the LLM needs to be called during training. While previous approaches call LLMs multiple times per step, resulting in thousands of calls per episode, EnvGen only requires a few LLM calls (e.g., 4 in total) throughout the entire RL agent training process. This reduction in LLM calls makes the training process more efficient and cost-effective compared to methods like SPRING, which can incur high costs due to their extensive use of LLM queries.

What are the implications of reducing the number of LLM calls in training

Reducing the number of LLM calls in training has several implications for reinforcement learning processes. Firstly, it leads to improved efficiency as fewer resources are required for each training cycle. This efficiency translates into faster training times and lower computational costs, making it more accessible for researchers and developers working with limited resources.
Additionally, by minimizing the reliance on frequent LLM calls, EnvGen promotes better scalability of AI models. The reduced dependency on continuous interactions with large language models allows for smoother integration into real-world applications where speed and resource optimization are crucial factors.
Furthermore, decreasing the number of LMM calls can enhance overall model performance by focusing on generating adaptive environments that target specific weaknesses or skills that need improvement in RL agents. This targeted approach ensures that valuable insights from the language model are utilized effectively without unnecessary overhead.

How might adaptive environment generation impact the scalability of AI training

The adaptive environment generation facilitated by EnvGen has significant implications for enhancing the scalability of AI training processes. By dynamically adjusting environments based on feedback from RL agent performance, EnvGen enables a more personalized and tailored learning experience for agents. This adaptability ensures that agents receive focused training on areas where they struggle or require improvement.
Moreover, adaptive environment generation contributes to enhanced generalization capabilities within AI systems. By exposing agents to diverse scenarios designed specifically to address their weaknesses or challenges, they develop robust problem-solving skills across various tasks and domains. This adaptability fosters versatility and agility in AI models when faced with new or unseen situations.
Overall, adaptive environment generation through tools like EnvGen plays a vital role in scaling up AI training efforts by optimizing learning experiences based on individual agent requirements while promoting efficient utilization of resources and improving overall performance outcomes.