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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Experimental quantum speed-up in reinforcement learning agents

Authors: Saggio, Valeria; Asenbeck, Beate E.; Hamann, Arne; Strömberg, Teodor; Schiansky, Peter; Dunjko, Vedran; Friis, Nicolai; +6 Authors

Experimental quantum speed-up in reinforcement learning agents

Abstract

The content of the text files can be used to reproduce the experimental plots presented in the manuscript. All the files contain sequences of numbers "0" and "1", which represent the non-reward and reward assigned to the agent after every epoch, respectively. In more detail, three different cases are shown: classical strategy, quantum strategy, and combined strategy. 1. Classical strategy The text files named "Classical_10agents_1000rounds.txt", "Classical_139agents_1000rounds.txt", and "Classical_16agents_1000rounds.txt" consist of 10, 139, and 16 consecutive sequences of 1000 numbers, respectively. Each of these sequences corresponds to one agent playing 1000 epochs. Therefore, there are in total 10+139+16=165 agents, each playing 1000 epochs. Merging these files and averaging the epoch outcomes 0 or 1 over all the 165 agents will make it possible to reproduce the behaviour of the average reward for the classical strategy. 2. Quantum strategy The text file named "Quantum_165agents_500rounds.txt" contains 165 arrays of length 500. Similarly to the previous case, the content of each array is progressively created every time the agent plays an epoch. 500 epochs are played. Also in this case, averaging the different outcomes over all the 165 agents will reproduce the behaviour of the average reward for the quantum strategy. 3. Combined strategy In this case, one file is acquired for the quantum strategy, and one for the classical strategy. These files are named "Combined_quantum_165agents.txt" and "Combined_classical_165agents.txt", respectively. Also in this case, 165 arrays (representing the 165 agents) are present in both files. However, the length of these arrays is not always the same. Taking as an example the first array of both the quantum and the classical files, there are 47 elements in the quantum case and 906 in the classical case. This means that the agent has played 47 quantum epochs before switching to a classical strategy. Therefore, in order to combine these two cases, one needs to merge the quantum array with the classical array, where only the even-indexed elements have been selected in the classical file. In this way, one obtains 47+906/2=500 epochs. The same procedure applies to the rest of the arrays. After 165 arrays of length 500 are obtained, the average over all the agents can be performed, and the plot for the combined strategy can thus be reproduced.

Keywords

Reinforcement learning, Quantum computing

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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