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Operator World Models for Reinforcement Learning

Computer Science

Operator World Models for Reinforcement Learning

P. Novelli, M. Pontil, et al.

Policy Mirror Descent (PMD) is powerful but hard to apply in Reinforcement Learning because action-value functions are not directly accessible. This work learns a world model via conditional mean embeddings and—using operator-theoretic matrix operations—derives closed-form action-value estimates. Combining these with PMD yields POWR, an RL algorithm with proven global convergence; this research was conducted by Pietro Novelli, Massimiliano Pontil, Marco Pratticò, and Carlo Ciliberto.... show more
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