Symphony of experts: orchestration with adversarial insights in reinforcement learning

• URL: http://arxiv.org/abs/2310.16473v1
• Date: Wed, 25 Oct 2023 08:53:51 GMT
• Title: Symphony of experts: orchestration with adversarial insights in reinforcement learning
• Authors: Matthieu Jonckheere (LAAS), Chiara Mignacco (LMO, CELESTE), Gilles Stoltz (LMO, CELESTE)
• Abstract summary: We explore the concept of orchestration, where a set of expert policies guides decision-making. We extend the analysis of natural policy gradient to arbitrary adversarial aggregation strategies. A key point of our approach lies in its arguably more transparent proofs compared to existing methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structured reinforcement learning leverages policies with advantageous properties to reach better performance, particularly in scenarios where exploration poses challenges. We explore this field through the concept of orchestration, where a (small) set of expert policies guides decision-making; the modeling thereof constitutes our first contribution. We then establish value-functions regret bounds for orchestration in the tabular setting by transferring regret-bound results from adversarial settings. We generalize and extend the analysis of natural policy gradient in Agarwal et al. [2021, Section 5.3] to arbitrary adversarial aggregation strategies. We also extend it to the case of estimated advantage functions, providing insights into sample complexity both in expectation and high probability. A key point of our approach lies in its arguably more transparent proofs compared to existing methods. Finally, we present simulations for a stochastic matching toy model.

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