@inproceedings{
novelli2024operator,
title={Operator World Models for Reinforcement Learning},
author={Pietro Novelli and Marco Prattic{\`o} and massimiliano pontil and Carlo Ciliberto},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kbBjVMcJ7G}
}
Policy Mirror Descent (PMD) is a powerful and theoretically sound methodology for sequential decision-making. However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions. We address this challenge by introducing a novel approach based on learning a world model of the environment using conditional mean embeddings. Leveraging tools from operator theory we derive a closed-form expression of the action-value function in terms of the world model via simple matrix operations. Combining these estimators with PMD leads to POWR, a new RL algorithm for which we prove convergence rates to the global optimum. Preliminary experiments in finite and infinite state settings support the effectiveness of our method.
Operator World Models for Reinforcement Learning
Pietro Novelli Marco Pratticò massimiliano pontil Carlo Ciliberto