Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning

PRISM is a new reinforcement learning framework that enables agents to transfer strategies zero-shot by clustering encoder features into causally validated concepts and aligning them between different agents trained with various algorithms. This matters because it allows for efficient knowledge transfer in strategic domains, enhancing performance without requiring extensive retraining, which can be particularly beneficial for complex games like Go but less effective in more chaotic environments like Atari Breakout.

Read the full article at arXiv cs.LG (ML)

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