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About Parametrized Quantum Policies For Reinforcement Learning
This document proposes using parametrized quantum circuits (PQCs) as policies for reinforcement learning agents. It shows that PQC policies can solve standard benchmark tasks comparable to deep neural network policies. It also constructs reinforcement learning environments where PQC policies provably learn faster than classical algorithms, based on the quantum algorithm for the discrete logarithm problem. The document contributes new model constructions for quantum-enhanced reinforcement learning agents and demonstrates their learning abilities both empirically and theoretically.
Detailed Information
| Author: | ['Lakshika Rathi'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 33 |
| Language: | English |
| Format: | |
| Price: | FREE |
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