A hybrid learning agent for episodic learning tasks with unknown target distance

Abstract

The "hybrid agent for quantum-accessible reinforcement learning," as defined in (Hamann and Wölk New J Phys 24:033044 2022), provides a proven quasi-quadratic speedup and is experimentally tested. However, the standard version can only be applied to episodic learning tasks with fixed episode length. In many real-world applications, the information about the necessary number of steps within an episode to reach a defined target is not available in advance and especially before reaching the target for the first time. Furthermore, in such scenarios, classical agents have the advantage of observing at which step they reach the target. How to best deal with an unknown target distance in classical and quantum reinforcement learning and whether the hybrid agent can provide an advantage in such learning scenarios is unknown so far. In this work, we introduce a hybrid agent with a stochastic episode length selection strategy to alleviate the need for knowledge about the necessary episode length. Through simulations, we test the adapted hybrid agent’s performance versus classical counterparts with and without similar episode selection strategies. Our simulations demonstrate a speedup in certain scenarios due to our developed episode length selection strategy for classical learning agents as well as an additional speedup for our resulting hybrid learning agent.