拡散拡張エージェント：効率的な探索と転移学習のためのフレームワーク
Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning
大規模言語モデル・視覚言語モデル・拡散モデルを組み合わせ、過去の経験を拡散モデルで目標指示に合わせて変換する後知恵経験拡張により、強化学習のサンプル効率と転移学習を改善するフレームワークを提案。
著者: Norman Di Palo, Leonard Hasenclever, Jan Humplik, Arunkumar Byravan
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
We introduce Diffusion Augmented Agents (DAAG), a novel framework that leverages large language models, vision language models, and diffusion models to improve sample efficiency and transfer learning in reinforcement learning for embodied agents. DAAG hindsight relabels the agent's past experience by using diffusion models to transform videos in a temporally and geometrically consistent way to align with target instructions with a technique we call Hindsight Experience Augmentation. A large language model orchestrates this autonomous process without requiring human supervision, making it well-suited for lifelong learning scenarios. The framework reduces the amount of reward-labeled data needed to 1) finetune a vision language model that acts as a reward detector, and 2) train RL agents on new tasks. We demonstrate the sample efficiency gains of DAAG in simulated robotics environments involving manipulation and navigation. Our results show that DAAG improves learning of reward detectors, transferring past experience, and acquiring new tasks - key abilities for developing efficient lifelong learning agents. Supplementary material and visualizations are available on our website https://sites.google.com/view/diffusion-augmented-agents/
