ASPECT: Analogical Semantic Policy Execution via Language Conditioned Transfer
Summary: arXiv:2604.08355v2 Announce Type: replace
Abstract
Reinforcement Learning (RL) agents often struggle to generalize knowledge to new tasks, even those structurally similar to ones they have mastered. Although recent approaches have attempted to mitigate this issue via zero-shot transfer, they are often constrained by predefined, discrete class systems, limiting their adaptability to novel or compositional task variations.
Introduction
In the realm of artificial intelligence, the ability of agents to apply learned knowledge to new, unseen tasks is essential for robust performance. Traditional reinforcement learning methods have made significant progress, yet they face limitations when confronted with tasks that, while similar, differ in specific structural aspects. The challenge lies in the rigid frameworks that categorize tasks into discrete classes, which hinders the agent’s ability to adapt to variations that fall outside these classifications.
Proposed Solution
We propose a significantly more generalized approach, replacing discrete latent variables with natural language conditioning via a text-conditioned Variational Autoencoder (VAE). This innovation shifts the paradigm from rule-based task execution to a more fluid, language-driven mechanism.
Core Innovation
Our core innovation utilizes a Large Language Model (LLM) as a dynamic semantic operator at test time. Instead of adhering to rigid rules that may not apply to every situation, our agent engages with the LLM to semantically remap the description of the current observation. This process aligns the observation with the source task, allowing for a more nuanced understanding of the task at hand.
Mechanism of Action
The source-aligned caption generated through this interaction conditions the VAE to produce an imagined state that is compatible with the agent’s original training. This mechanism enables direct policy reuse, allowing the agent to leverage previously gained knowledge effectively. By integrating the flexible reasoning capabilities of LLMs into the reinforcement learning framework, we can achieve zero-shot transfer across a wide array of complex and novel analogous tasks.
Benefits
The advantages of this approach include:
- Enhanced Flexibility: Agents can adapt to a broader range of tasks without the need for extensive retraining.
- Improved Generalization: The use of natural language allows for more nuanced understanding and execution of tasks.
- Efficient Knowledge Transfer: Direct policy reuse means that agents can apply prior learning to new contexts with ease.
Conclusion
In conclusion, our approach represents a significant advancement in the field of reinforcement learning by moving beyond the constraints of fixed category mappings. By employing a language-conditioned model, we pave the way for more adaptable and intelligent agents capable of addressing a wider variety of challenges. For those interested, code and videos demonstrating this approach are available here.