TLDR: The ICP AD framework introduces a novel approach for rapidly adapting skill-based reinforcement learning policies to new environments with limited data and no model updates. It leverages cross-domain skill diffusion to learn universal ‘prototype skills’ and a domain-specific skill adapter, guided by dynamic domain prompting. Experiments in robotic manipulation (Metaworld) and autonomous driving (CARLA) demonstrate ICP AD’s superior performance and robustness in adapting to diverse environmental dynamics, agent embodiments, and task horizons compared to existing methods.
The field of reinforcement learning (RL) has seen incredible advancements, allowing AI agents to master complex tasks. However, a significant hurdle remains: adapting policies learned in one environment (source domain) to a completely different one (target domain), especially when new data is scarce and direct interaction with the new environment is limited. This challenge is particularly pronounced in complex, long-horizon tasks, where traditional methods often struggle.
A new framework, In-Context Policy Adaptation (ICP AD), addresses this critical issue by enabling rapid adaptation of skill-based RL policies to diverse target domains. What makes ICP AD stand out is its ability to adapt without requiring any model updates and using only a small amount of data from the target domain. This is a game-changer for real-world applications where retraining models for every new scenario is impractical or impossible.
How ICP AD Works: A Two-Phase Approach
The ICP AD framework operates in two main phases: offline learning and in-context adaptation. During the offline learning phase, the system establishes a set of ‘domain-agnostic prototype skills’ and a ‘domain-grounded skill adapter’. Think of prototype skills as a universal language or a set of fundamental behaviors that can be applied across different domains. The skill adapter then translates these universal skills into specific actions tailored to a particular domain.
This is achieved through a novel ‘cross-domain skill diffusion scheme’. This scheme ensures that the prototype skills are consistent across various domains, allowing the skill adapter to generate action sequences that accurately reflect both the learned skills and the characteristics of the target domain. Essentially, it learns how to make skills transferable and adaptable.
In the subsequent in-context adaptation phase, policies that were trained using these prototype skills can be quickly adapted to new, unseen target domains. This is facilitated by a ‘dynamic domain prompting scheme’. This scheme provides real-time guidance to the diffusion-based skill adapter, helping it align better with the target domain using only a few examples (few-shot data). This means the AI can quickly understand and perform tasks in a new environment with minimal new information.
Outperforming the Competition
The effectiveness of ICP AD was rigorously tested in two challenging environments: robotic manipulation tasks in Metaworld and autonomous driving scenarios in CARLA. These environments represent diverse challenges, including variations in environment dynamics, agent embodiment (e.g., different vehicle types), and task horizon (short vs. long tasks).
In experiments involving varied environmental dynamics, such as different levels of action noise and wind in Metaworld, ICP AD consistently outperformed state-of-the-art baselines. For instance, it achieved an average success rate 14.0% higher than its closest competitor, DCMRL. While other methods saw significant performance drops as domain disparity increased, ICP AD showed only a slight degradation, demonstrating its robust generalization capabilities.
For autonomous driving in CARLA, where challenges included different vehicle embodiments and weather conditions, ICP AD again showed superior performance, achieving 11.6% to 21.6% higher normalized returns than DCMRL. This highlights the framework’s ability to simplify environmental complexity by adapting at an abstract skill level, making it highly effective in complex, real-world settings.
Even with extremely limited data availability for new tasks (as low as 8% of tasks having demonstrations), ICP AD maintained strong performance, showing only a 6.8% decline, compared to a 32.0% decrease for DCMRL. This underscores the efficiency of its unified, domain-wise policy adaptation strategy.
Furthermore, ICP AD demonstrated its flexibility by successfully integrating language-based prompts, allowing agents to follow human instructions and achieve comparable performance to using sub-trajectories. This opens doors for more interactive human-agent systems.
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The Future of Policy Adaptation
The ICP AD framework represents a significant step forward in making reinforcement learning policies more adaptable and robust in diverse, data-constrained environments. By learning domain-agnostic prototype skills and employing a dynamic prompting mechanism, it offers a powerful solution for rapid, in-context policy adaptation without the need for extensive retraining. The researchers plan to extend this framework to accommodate multi-modal datasets, aiming to explore semantic interpretability and alignment across vastly different tasks and domains for embodied control applications. You can read the full research paper here.
