Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
Summary: arXiv:2604.02353v1 Announce Type: cross
Abstract: We present PRISM (Policy Reuse via Interpretable Strategy Mapping), a framework that grounds reinforcement learning agents’ decisions in discrete, causally validated concepts and uses those concepts as a zero-shot transfer interface between agents trained with different algorithms.
Introduction
The domain of reinforcement learning (RL) has witnessed significant advancements in recent years, yet challenges remain in transferring learned strategies across agents trained under different conditions. The introduction of PRISM aims to tackle this problem by providing a structured framework for policy reuse that is both interpretable and effective.
Key Features of PRISM
- Concept Clustering: PRISM utilizes K-means clustering to group each agent’s encoder features into K distinct concepts. This approach allows for a clearer understanding of the underlying strategies that drive agent behavior.
- Causal Validation: The framework employs causal intervention techniques to establish that these concepts are directly responsible for agent actions. The results are compelling, showing that overriding concept assignments influences the chosen action in 69.4% of the interventions.
- Concept Importance: Interestingly, the frequency of concept usage does not correlate with its importance. For example, the most frequently used concept (C47) has a win-rate drop of only 9.4% when ablated, while the less frequent concept (C16) results in a drastic win-rate collapse from 100% to 51.8% upon removal.
Strategic Knowledge Transfer
One of the most notable features of PRISM is its ability to transfer strategic knowledge between agents through optimal bipartite matching of concepts. This zero-shot transfer capability proves essential in scenarios where agents trained under different algorithms need to collaborate or compete effectively.
Experimental Validation
To validate the effectiveness of PRISM, experiments were conducted using a Go 7×7 environment with three independently trained agents. The results demonstrated that concept transfer achieved impressive win rates of 69.5%±3.2% and 76.4%±3.4% against a standard engine across two successful transfer pairs, compared to a mere 3.5% for a random agent and 9.2% without alignment.
Implications for Future Research
The findings suggest that the success of transfer is contingent upon the strength of the source policy. Interestingly, the quality of geometric alignment did not correlate with transfer success, indicating that the structural properties of the domain play a crucial role. This distinction paves the way for future research to explore other domains where strategic states are inherently discrete.
Conclusion
In conclusion, PRISM represents a significant advancement in the field of reinforcement learning, offering a framework that not only enhances policy reuse but also fosters interpretability in agent decision-making. As the field continues to evolve, frameworks like PRISM will be pivotal in bridging the gap between different RL paradigms, ultimately leading to more robust and adaptable artificial intelligence systems.