Core Concepts
Variational quantum circuits improve data efficiency in batch reinforcement learning.
Abstract
Introduction
Deep reinforcement learning (DRL) requires extensive data and environment interactions.
Batch RL trains solely on pre-collected datasets, facing challenges of data inefficiency.
Advancements in Quantum Computing
Quantum models may require less data for training compared to classical methods.
Investigation into utilizing variational quantum circuits (VQCs) in batch RL algorithms.
Proposed Algorithm
Introducing a novel data re-uploading scheme by cyclically shifting input variables order.
Evaluation of algorithm efficiency on OpenAI CartPole environment compared to classical neural network-based discrete BCQ.
Theoretical Background
Overview of reinforcement learning framework and Markov Decision Problems (MDPs).
Introduction to deep Q-learning using DNNs as function approximators for Q-functions.
Variational Quantum Circuits for RL
Explanation of VQCs as function approximators and their components like data encoding, variational layers, and measurement.
Efficient Gradient Estimation on Quantum Devices
Challenges in computing gradients on quantum devices and optimization schemes like SPSA.
Related Work
Summary of quantum reinforcement learning research and relevant batch RL literature.
Stats
量子モデルは、古典的手法と比較してトレーニングに少ないデータを必要とする可能性がある。
VQCは、バッチ強化学習アルゴリズムでのデータ効率を向上させる。
Quotes
"Recent advancements in quantum computing suggest that quantum models might require less data for training compared to classical methods."
"Introducing a novel data re-uploading scheme by cyclically shifting the order of input variables in the data encoding layers."