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While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited.
Zanette, A · 1901
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Distributional property testing in a quantum world
Gilyén, A · 1902
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Sample-optimal parametric q-learning using linearly additive features
Yang, L · 1902
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Non-asymptotic gap-dependent regret bounds for tabular mdps
Simchowitz, M · 1905
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Reinforcement learning in feature space: Matrix bandit, kernels, and regret bound
Yang, L · 1905
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Provably efficient reinforcement learning with linear function approximation
Jin, C · 1907
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Sample complexity of reinforcement learning using linearly combined model ensembles
Modi, A · 1910
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Provably efficient exploration in policy optimization
Cai, Q · 1912
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Elementary gates for quantum computation
Barenco, A · 1995
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Quantum mechanics helps in searching for a needle in a haystack
Grover, L. K · 1997
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Finite-sample convergence rates for Q-learning and indirect algorithms
Kearns, M · 1998
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The quantum query complexity of approximating the median and related statistics
Nayak, A · 1999
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Quantum amplitude amplification and estimation
Brassard, G · 2002
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Casalé, B · 2002
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Creating superpositions that correspond to efficiently integrable probability distributions
Grover, L · 2002
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Quantum computation and quantum information
Nielsen, M. A · 2002
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On the sample complexity of reinforcement learning
Kakade, S. M · 2003
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Model-based reinforcement learning with value-targeted regression
Ayoub, A · 2006
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Q-learning with logarithmic regret
Yang, K · 2006
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Quantum exploration algorithms for multi-armed bandits
Wang, D · 2007
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Near-optimal regret bounds for reinforcement learning
Auer, P · 2008
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Quantum-accessible reinforcement learning beyond strictly epochal environments
Hamann, A · 2008
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Zhang, Z · 2009
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Near-optimal regret bounds for reinforcement learning
Jaksch, T · 2010
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Logarithmic regret for reinforcement learning with linear function approximation
He, J · 2011
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Framework for learning agents in quantum environments
Dunjko, V · 2015
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Quantum speedup of Monte Carlo methods
Montanaro, A · 2015
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An introduction to quantum machine learning
Schuld, M · 2015
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Quantum sub-Gaussian mean estimator
Hamoudi, Y · 2021
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Randomized exploration in reinforcement learning with general value function approximation
Ishfaq, H · 2021
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Bellman eluder dimension: New rich classes of RL problems, and sample-efficient algorithms
Jin, C · 2021
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Experimental quantum speed-up in reinforcement learning agents
Saggio, V · 2021
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Quantum probability oracles & multidimensional amplitude estimation
van Apeldoorn, J · 2021
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Fine-grained gap-dependent bounds for tabular MDPs via adaptive multi-step bootstrap
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Dunjko, V · 2016
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Guest column: a survey of quantum learning theory
Arunachalam, S · 2017
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Minimax regret bounds for reinforcement learning
Azar, M. G · 2017
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Biamonte, J · 2017
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Advances in quantum reinforcement learning
Dunjko, V · 2017
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Contextual decision processes with low bellman rank are PAC-learnable
Jiang, N · 2017
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Quantum gradient estimation and its application to quantum reinforcement learning
Cornelissen, A · 2018
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Xu, H · 2021
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Nearly minimax optimal reinforcement learning for linear mixture markov decision processes
Zhou, D · 2021
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A general framework for sample-efficient function approximation in reinforcement learning
Chen, Z · 2022
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Quantum reinforcement learning via policy iteration
Cherrat, E. A · 2022
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Near-optimal quantum algorithms for multivariate mean estimation
Cornelissen, A · 2022
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Asymptotic instance-optimal algorithms for interactive decision making
Dong, K · 2022
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Performance analysis of a hybrid agent for quantum-accessible reinforcement learning
Hamann, A · 2022
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Quantum policy gradient algorithms
Jerbi, S · 2022
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Li, T · 2022
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Lumbreras, J · 2022
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A survey on quantum reinforcement learning
Meyer, N · 2022
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Quantum multi-armed bandits and stochastic linear bandits enjoy logarithmic regrets
Wan, Z · 2022
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Wiedemann, S · 2022
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Nearly optimal policy optimization with stable at any time guarantee
Wu, T · 2022
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Horizon-free reinforcement learning in polynomial time: the power of stationary policies
Zhang, Z · 2022
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Gec: A unified framework for interactive decision making in mdp, pomdp, and beyond
Zhong, H · 2022
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Quantum computing provides exponential regret improvement in episodic reinforcement learning
Ganguly, B · 2023
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