Composing Reinforcement Learning Policies, with Formal Guarantees

• URL: http://arxiv.org/abs/2402.13785v2
• Date: Mon, 10 Mar 2025 11:38:38 GMT
• Title: Composing Reinforcement Learning Policies, with Formal Guarantees
• Authors: Florent Delgrange, Guy Avni, Anna Lukina, Christian Schilling, Ann Nowé, Guillermo A. Pérez,
• Abstract summary: We propose a novel framework to controller design in environments with a two-level structure.<n>The framework "separates concerns" by using different design techniques for low- and high-level tasks.
• Score: 15.690880632229202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel framework to controller design in environments with a two-level structure: a known high-level graph ("map") in which each vertex is populated by a Markov decision process, called a "room". The framework "separates concerns" by using different design techniques for low- and high-level tasks. We apply reactive synthesis for high-level tasks: given a specification as a logical formula over the high-level graph and a collection of low-level policies obtained together with "concise" latent structures, we construct a "planner" that selects which low-level policy to apply in each room. We develop a reinforcement learning procedure to train low-level policies on latent structures, which unlike previous approaches, circumvents a model distillation step. We pair the policy with probably approximately correct guarantees on its performance and on the abstraction quality, and lift these guarantees to the high-level task. These formal guarantees are the main advantage of the framework. Other advantages include scalability (rooms are large and their dynamics are unknown) and reusability of low-level policies. We demonstrate feasibility in challenging case studies where an agent navigates environments with moving obstacles and visual inputs.

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