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

Abstract

Recent state-of-the-art approaches for embodied learning via interaction directly employ large language models (LLMs) as agents to determine the next steps in an environment. Due to their world knowledge and reasoning capabilities, LLM agents achieve stronger performance than previous smaller agents based on reinforcement learning (RL); however, frequently calling LLMs is slow and expensive. This begs an interesting question – Instead of directly employing LLMs as embodied agents, can we use LLMs’ reasoning capabilities to adaptively create training environments to help smaller embodied RL agents learn useful skills that they are weak at? In this work, we propose EnvGen, a novel framework to address this question. First, we prompt an LLM to generate training environments that allow agents to quickly learn different tasks in parallel. Concretely, the LLM is given the task description and environment simulator objectives that the agents should learn and is then asked to generate a set of environment configurations (e.g., different terrains, items initially given to agents, chance of finding certain objects, etc.). Next, we train a small RL agent in a mixture of the original and LLM-generated environments. Then, we enable the LLM to continuously adapt the generated environments to progressively improve the skills that the agent is weak at, by providing feedback to the LLM in the form of the agent’s performance. We demonstrate the usefulness of EnvGen with comprehensive experiments in Crafter and Heist game environments. We find that a small RL agent trained with EnvGen can outperform SOTA methods, including a GPT-4 agent, and learns longhorizon tasks significantly faster. We also show qualitatively how the LLM adapts training environments to help improve RL agents’ weaker skills over time. Additionally, EnvGen is substantially more efficient as it only uses a small number of LLM calls (e.g., 4 in total), whereas LLM agents require one or more LLM calls per step (resulting in thousands of LLM calls per episode). Lastly, we present detailed ablation studies for EnvGen’s design choices.

Jaemin Cho

PhD student @ UNC Chapel Hill

PhD @ UNC Chapel Hill. Interested in multimodal machine learning.