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QI: Fachverband Quanteninformation

QI 18: Poster II

QI 18.33: Poster

Wednesday, March 20, 2024, 11:00–14:30, Poster A

Extending the Hybrid Agent for Reinforcement Learning Beyond Fixed-Length Scenarios — •Oliver Sefrin and Sabine Wölk — Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Quantentechnologien, 89081 Ulm

In Quantum Reinforcement Learning, the "hybrid agent for quantum-accessible reinforcement learning" (Hamann and Wölk, 2022) provides a quadratic speed-up in terms of sample complexity over classical algorithms. This hybrid agent may be used in deterministic and strictly episodic environments, for which the maze problem is a standard example.

With the current algorithm, however, the episode length (i.e., the number of actions to be played in an episode) is a hyperparameter which needs to be set. For scenarios such as mazes with an unknown distance towards the goal, this poses a problem, since a feasible episode length value is not known initially.

In this work, we propose an adaption to the hybrid algorithm that uses a variable episode length selection strategy, allowing its usage in a wider range of maze problem scenarios. We test our novel approach against classical agents in various maze scenarios. Finally, we reason about conditions for which a quantum advantage persists.

Keywords: Hybrid Algorithm; Reinforcement Learning; Amplitude Amplification