BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading

arXiv:2305.00905v2 Announce Type: replace-cross
Abstract: Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerbated in the case of batch RL, where the agent is trained solely on a pre-collected dataset without environment interactions. Recent advancements in quantum computing suggest that quantum models might require less data for training compared to classical methods. In this paper, we investigate this potential advantage by proposing a batch RL …

abstract agent arxiv case challenge cs.lg data dataset environment interactions making process q-learning quant-ph quantum reinforcement reinforcement learning training type