Performance analysis of a hybrid agent for quantum-accessible reinforcement learning

Understand

In the last decade quantum machine learning has provided fascinating and fundamental improvements to supervised, unsupervised and reinforcement learning.

• In reinforcement learning, a so-called agent is challenged to solve a task given by some environment.
• The agent learns to solve the task by exploring the environment and exploiting the rewards it gets from the environment.
• For some classical task environments, such as deterministic strictly epochal environments, an analogue quantum environment can be constructed which allows to find rewards quadratically faster by applying quantum algorithms.