Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model
Researchers have introduced new quantum algorithms for reinforcement learning, a subfield of machine learning where an agent interacts with an environment to maximize rewards. The study focuses on Markov Decision Processes (MDPs) in both finite-horizon and infinite-horizon discounted settings. The proposed algorithms combine standard value iteration with quantum subroutines such as quantum mean estimation and quantum maximum finding, enhanced by techniques from sample-optimal classical algorithms. These new methods achieve query complexities that improve upon previous works, approaching established quantum lower bounds. The work is available on arXiv under the identifier 2608.02826.
Key facts
- The paper proposes new quantum algorithms for reinforcement learning.
- It studies finite-horizon and infinite-horizon discounted Markov Decision Processes.
- The algorithms combine value iteration with quantum mean estimation and quantum maximum finding.
- Techniques from sample-optimal classical algorithms are incorporated.
- The resulting query complexities improve upon previous works.
- The improvements approach established quantum lower bounds.
- The paper is available on arXiv with identifier 2608.02826.
- The research is in the field of quantum machine learning.
Entities
Institutions
- arXiv