Скачать с ютуб Synthetic Video: LLM + VLM + Diffusion Mod = DAAG в хорошем качестве

Synthetic Video: LLM + VLM + Diffusion Mod = DAAG

The Diffusion Augmented Agents (DAAG) framework introduces a novel approach that leverages large language models (LLMs), vision language models (VLMs), and diffusion models to significantly enhance the efficiency of sample collection and transfer learning in reinforcement learning for embodied agents. This method, named Hindsight Experience Augmentation (HEA), relabels past experiences by using diffusion models to transform videos in a temporally and geometrically consistent manner. This alignment with target instructions allows the autonomous process to be orchestrated by an LLM without human supervision, making it highly suitable for lifelong learning scenarios. The DAAG framework thus reduces the dependency on reward-labeled data for fine-tuning VLMs, which function as reward detectors, and for training RL agents on new tasks, demonstrating improved learning efficiency in robotic manipulation and navigation tasks. The DAAG framework operates through a combination of three principal components: an LLM that acts as the main controller, querying and guiding both the VLM and the diffusion model; a VLM, specifically CLIP, which is fine-tuned to detect rewards and subgoals in a visually augmented dataset; and a diffusion model, based on Stable Diffusion, which modifies visual observations to create synthetic successful episodes. This integration enables the autonomous generation of augmented data that aligns with new task requirements, thereby enhancing the agent's ability to learn from limited and past experiences. Through this method, DAAG effectively reuses and repurposes data from various tasks, which is crucial for developing agents that can adapt and learn efficiently over a lifetime. Empirical evaluations of DAAG highlight its ability to significantly improve task learning and transfer efficiency. In the conducted experiments, DAAG demonstrated superior performance in fine-tuning VLMs for novel tasks, exploring and learning new tasks more efficiently, and transferring experience from past tasks to new ones. The autonomous augmentation of unsuccessful episodes into successful trajectories through HEA notably enhances exploration efficiency and robustness in varying environments. This method represents a significant advancement in overcoming data scarcity challenges in reinforcement learning for embodied agents, paving the way for more capable and adaptable lifelong learning agents. Referenced paper by Google DeepMind: DIFFUSION AUGMENTED AGENTS: A FRAMEWORK FOR EFFICIENT EXPLORATION AND TRANSFER LEARNING https://arxiv.org/pdf/2407.20798 #airesearch #newtechnology #science