Operator World Models for Reinforcement Learning

• URL: http://arxiv.org/abs/2406.19861v2
• Date: Wed, 30 Oct 2024 21:58:12 GMT
• Title: Operator World Models for Reinforcement Learning
• Authors: Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto,
• Abstract summary: Policy Mirror Descent (PMD) is a powerful and theoretically sound methodology for sequential decision-making. It is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions. We introduce a novel approach based on learning a world model of the environment using conditional mean embeddings.
• Score: 37.69110422996011
• License:
• Abstract: 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

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