Recent advancements in utilizing large vision language models (VLMs) and language models (LLMs) have significantly impacted reinforcement learning (RL) and robotics. These models have demonstrated their utility in learning robot policies, high-level reasoning, and automating the generation of reward functions for policy learning. This progress has notably reduced the need for domain-specific knowledge typically required from RL researchers.
However, despite these advancements, many steps within the experimental workflow of training policies via RL still necessitate human intervention. These steps include determining when an experiment has concluded and constructing task curricula to facilitate learning target tasks. While some research has attempted to automate individual steps in this process, such as automated training and evaluation of standard machine learning tasks or automated curriculum building, these approaches often consider each step in isolation, utilizing models specifically trained for a single task. The challenge remains to develop a more holistic, automated approach that can seamlessly integrate these various steps in the RL experimental workflow, reducing the need for human intervention across the entire process.
In the realm of science and engineering automation, LLM-empowered agents are being developed to assist in software engineering tasks, from interactive pair-programming to end-to-end software development. Similarly, in scientific research, LLM-based agents are being employed to generate research directions, analyze literature, automate scientific discovery, and conduct machine learning experiments. For embodied agents, particularly in robotics, LLMs are being utilized to write policy code, decompose high-level tasks into subtasks, and even propose tasks for open-ended exploration. Notable examples include the Voyager agent for Minecraft and systems like CaP and SayCan for robotics tasks. These approaches demonstrate the potential of LLMs in automating complex reasoning and decision-making processes in physical environments. However, most existing work focuses on automating individual steps or specific domains. The challenge remains in developing integrated systems that can automate entire experimental workflows, particularly in reinforcement learning for robotics, where task proposal, decomposition, execution, and evaluation need to be seamlessly combined.
DeepMind Researchers propose an innovative agent architecture that automates key aspects of the RL experiment workflow, aiming to enable automated mastery of control domains for embodied agents. This system utilizes a VLM to perform tasks typically handled by human experimenters, including:
1. Monitoring and analyzing experiment progress
2. Proposing new tasks based on the agent’s past successes and failures
3. Decomposing tasks into sequences of subtasks (skills)
4. Retrieving appropriate skills for execution
This approach enables the system to build automated curricula for learning, representing one of the first proposals for a system that utilizes a VLM throughout the entire RL experiment cycle.
The researchers have developed a prototype of this system, using a standard Gemini model without additional fine-tuning. This model provides a curriculum of skills to a language-conditioned Actor-Critic algorithm, guiding data collection to aid in learning new skills. The data collected through this method is effective for learning and iteratively improving control policies in a robotics domain. Further examination of the system’s ability to build a growing library of skills and assess the progress of skill training has yielded promising results. This suggests that the proposed architecture offers a potential blueprint for fully automated mastery of tasks and domains for embodied agents, marking a significant step towards more autonomous and efficient reinforcement learning systems in robotics.
To explore the feasibility of their proposed system, the researchers implemented its components and applied them to a simulated robotic manipulation task. The system architecture consists of several interacting modules:
1. Curriculum Module: This module retrieves images from the environment and incorporates them into goal proposal prompts. It decomposes goals into steps and retrieves skill captions. If all steps can be mapped to known skills, the skill sequence is sent to the embodiment module.
2. Embodiment Module: This uses a text-conditioned learned policy (Perceiver-Actor-Critic algorithm) to execute the skill sequences. Multiple instances of this module can perform episode rollouts simultaneously.
3. Analysis Module: Used outside the experiment loop to evaluate convergence points.
The modules interact through a chat-based interface in a Google Meet session, allowing for easy connection and human introspection. The curriculum module controls the program flow, changing skills at fixed intervals during rollouts.
For policy training, the system employs a Perceiver-Actor-Critic (PAC) model, which can be trained via offline reinforcement learning and is text-conditioned. This allows for the use of non-expert exploration data and relabeling of data with multiple reward functions. The high-level system utilizes a standard Gemini 1.5 Pro model, with prompts designed using the OneTwo Python library. The prompts include a small number of hand-designed exemplars with image data from previous experiments, covering proposal, decomposition, retrieval, and analysis tasks. This implementation demonstrates a practical approach to integrating VLMs into the RL workflow, enabling automated task proposal, decomposition, and execution in a simulated robotic environment.
The researchers evaluated their approach using a robotic block stacking task involving a 7-DoF Franka Panda robot in a MuJoCo simulator. They first trained a PAC model with 140M parameters on basic skills using a pre-existing dataset of 1M episodes. The Gemini-driven data collection process then generated 25k new episodes, exploring different VLM sampling temperatures and skill sets. The analysis module was used to determine optimal early stopping points and assess skill convergence. The curriculum module’s ability to work with a growing skill set was examined at various points in the experiment, demonstrating the system’s capacity for progressive learning and task decomposition.
The researchers have proposed an innovative agent architecture for reinforcement learning that utilizes a VLM to automate tasks typically performed by human experimenters. This system aims to enable embodied agents to autonomously acquire and master an expanding set of skills. The prototype implementation demonstrated several key capabilities:
- Proposing new tasks for explorationDecomposing tasks into skill sequencesAnalyzing learning progress
Despite some simplifications in the prototype, the system successfully collected diverse data for self-improvement of the control policy and learned new skills beyond its initial set. The curriculum showed adaptability in proposing tasks based on available skill complexity.
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