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• W3180284094 abstract "Despite the successes of recent works in quantum reinforcement learning, there are still severe limitations on its applications due to the challenge of encoding large observation spaces into quantum systems. To address this challenge, we propose using a neural network as a data encoder, with the Atari games as our testbed. Specifically, the neural network converts the pixel input from the games to quantum data for a Quantum Variational Circuit (QVC); this hybrid model is then used as a function approximator in the Double Deep Q Networks algorithm. We explore a number of variations of this algorithm and find that our proposed hybrid models do not achieve meaningful results on two Atari games - Breakout and Pong. We suspect this is due to the significantly reduced sizes of the hybrid quantum-classical systems." @default.
• W3180284094 created "2021-07-19" @default.
• W3180284094 creator A5008865789 @default.
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• W3180284094 date "2021-07-08" @default.
• W3180284094 modified "2023-09-27" @default.
• W3180284094 title "Playing Atari with Hybrid Quantum-Classical Reinforcement Learning" @default.
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• W3180284094 hasPublicationYear "2021" @default.
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