Quantum reinforcement learning : Comparing quantum annealing and gate-based quantum computing with classical deep reinforcement learning
article
In this paper, we present implementations of an annealing-based and a gate-based
quantum computing approach for finding the optimal policy to traverse a grid and
compare them to a classical deep reinforcement learning approach.We extended these
three approaches by allowing for stochastic actions instead of deterministic actions and
by introducing a new learning technique called curriculum learning. With curriculum
learning, we gradually increase the complexity of the environment and we find that it
has a positive effect on the expected reward of a traversal. We see that the number of
training steps needed for the two quantum approaches is lower than that needed for
the classical approach.
quantum computing approach for finding the optimal policy to traverse a grid and
compare them to a classical deep reinforcement learning approach.We extended these
three approaches by allowing for stochastic actions instead of deterministic actions and
by introducing a new learning technique called curriculum learning. With curriculum
learning, we gradually increase the complexity of the environment and we find that it
has a positive effect on the expected reward of a traversal. We see that the number of
training steps needed for the two quantum approaches is lower than that needed for
the classical approach.
Topics
TNO Identifier
982607
Source
Quantum Information Processing, 22
Article nr.
125