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Quantum Multiple Q Learning

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Figure

Figure 1. Environment used to test each algorithm. A represents the starting loca-tion of the agent in the environment, P represents the pit, where the agent receives a large negative reward, W represents the wall, which is a disallowed state, and G repres
Figure 3. Breakdown learning rate as function of the number of Q s a(, functions. The )breakdown learning rate is the minimum value of α where the algorithm fails to find the goal
Figure 5. Branching ratio of Q-learning, VQRL, and QQRL. The optimal path 33111 and the suboptimal path 31311 are shown in Figure 2
Figure 6. Expected value of initial state for Q-learning, VQRL, and QQRL. For VQRL, experiment, V s( ) is shown, but for QQRL and Q-learning V( )()maxs=arg max,aQ s a is shown for comparison
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