Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning

1. 1Cluster encoder features into discrete concepts using K-means
2. 2Establish causal relationships between concepts and agent decisions
3. 3Use concepts as a transfer interface between agents trained with different algorithms
4. 4Evaluate the effectiveness of PRISM in various reinforcement learning tasks

ML researchers and engineers on a team can benefit from PRISM as it allows for zero-shot transfer of policies between agents trained with different algorithms, improving efficiency and reducing training time

💡 PRISM enables zero-shot transfer of policies between agents trained with different algorithms by grounding decisions in discrete, causally validated concepts

🤖 Introducing PRISM: a framework for policy reuse in RL via interpretable strategy mapping! 🚀