Model-based Offline Quantum Reinforcement Learning

arXiv:2404.10017v1 Announce Type: cross
Abstract: This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the policy to be optimized are each implemented as variational quantum circuits. The model is trained by gradient descent to fit a pre-recorded data set. The policy is optimized with a gradient-free optimization scheme using the return estimate given by the model as the fitness function. This model-based approach allows, in principle, …

abstract algorithm arxiv benchmark cart circuits cs.ai cs.lg data data set gradient offline paper policy quant-ph quantum reinforcement reinforcement learning set type