Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning (2407.20798v1)

Abstract: We introduce Diffusion Augmented Agents (DAAG), a novel framework that leverages LLMs, vision LLMs, 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 LLM 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 LLM 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/

• The paper introduces an automated reward detector fine-tuning method that uses synthetic data from diffusion models to improve accuracy in unseen tasks.
• The framework employs Hindsight Experience Augmentation (HEA) to repurpose failed trajectories, significantly accelerating exploration and learning efficiency.
• The paper demonstrates that DAAG effectively transfers past experiences to new tasks, enhancing lifelong learning in simulated robotics environments.

Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning

Introduction

The paper presents Diffusion Augmented Agents (DAAG), a new framework for leveraging LLMs, vision LLMs (VLMs), and diffusion models to address key challenges in reinforcement learning (RL) for embodied AI agents. The proposed DAAG framework enhances sample efficiency and facilitates transfer learning. It employs a structured approach, termed as Hindsight Experience Augmentation (HEA), which repurposes the agent's past experiences by using diffusion models to align them with new task objectives. The process is fully autonomous and requires no human supervision, operating under the orchestration of an LLM. This framework aims to improve the efficiency of learning reward detectors, fostering efficient exploration and transfer learning in simulated robotics environments.

Main Contributions

The paper delineates three main contributions of DAAG:

1. Automated Reward Detector Fine-tuning: DAAG fine-tunes VLMs for reward detection using synthetic observations generated via diffusion models. The VLMs are trained not only on real data but also on artificially augmented data that corresponds to unseen tasks.
2. Efficient Exploration: The framework improves exploration efficiency by recognizing useful subgoals and repurposing failed trajectories. This accelerates the discovery of efficient strategies for new tasks.
3. Transfer Learning: DAAG effectively transfers past experiences to new tasks, modifying former trajectories using diffusion models to kickstart learning. This approach augments experience data for novel contexts, enhancing the agent's adaptability.

Methodology

The DAAG framework consists of several core components:

1. LLM: The LLM orchestrates the overall process. It breaks down high-level tasks into subgoals and queries both the VLM and the diffusion model to facilitate autonomous learning.
2. Vision LLM (VLM): Used for detecting rewards, the VLM is fine-tuned using a combination of real and synthetic data. This improves its capability to identify successful subgoal completions autonomously.
3. Diffusion Model: The diffusion model augments visual data by modifying observations to fit target tasks. It ensures both geometric and temporal consistency in the generated data, making it usable for RL scenarios.
4. Hindsight Experience Augmentation (HEA): HEA modifies past experiences to align them with new task objectives. The modified trajectories are then used for training RL agents, enhancing both sample efficiency and learning speed.

Experiments and Results

Fine-tuning Vision LLMs

The authors demonstrate DAAG's ability to fine-tune VLMs as reward detectors for unseen tasks. By leveraging synthetic observations from diffusion models, DAAG significantly outperforms baseline approaches that rely solely on real data. The empirical results show improved accuracy in detecting rewards for new tasks in environments such as RGB Stacking, Room (navigation), and Language Table (manipulation).

Efficient Exploration and Learning

DAAG's impact on exploration efficiency is tested in scenarios where agents learn tasks from scratch. The results indicate that DAAG accelerates learning by utilizing HEA to repurpose failed or partially successful trajectories. This allows agents to accumulate useful experience more rapidly compared to traditional methods.

Transfer Learning and Lifelong Learning

The framework is further tested in lifelong learning scenarios, where agents learn tasks sequentially. DAAG significantly improves forward and backward transfer, allowing agents to use past experiences to expedite learning of new tasks. This is particularly evident in the RGB Stacking and Room environments, where the use of augmented data from previous tasks substantial boosts performance on new tasks.

Practical Implications and Future Directions

The practical implications of DAAG are substantial for the domain of AI and robotics. By augmenting experience data, DAAG reduces the need for prohibitively high amounts of real-world interactions, which are often time-consuming and expensive. This advancement makes lifelong learning more feasible for embodied AI agents, opening pathways for their deployment in real-world applications such as service robots, autonomous vehicles, and industrial automation.

Future developments could explore a more nuanced interplay between different types of foundation models and investigate further improvements in the temporal and geometric consistency of generated observations. Enhancing the robustness and generalization capabilities of DAAG across even more diverse and complex tasks remains an interesting avenue for research.

Conclusion

The DAAG framework presents a significant stride in the field of reinforcement learning for embodied AI. By smartly using diffusion models and leveraging the power of large-scale vision and LLMs, DAAG achieves remarkable improvements in training efficiency, exploration, and transfer learning. The autonomous nature of DAAG, facilitated by LLM orchestration, underscores its potential for developing efficient lifelong learning agents. The research paves the way for more advanced and adaptable AI systems capable of learning from minimal direct experience, making substantial contributions to the ongoing efforts in autonomous systems and embodied AI.