DQN-based linear model.

<p>(A) Behavioral data were collected while subjects played the arcade games Breakout (top), Space Invaders (middle), and Enduro (bottom). The human-generated videos were processed by a baseline DQN (gray), Ape-X (green), and SEED (red). Each neuron of the output layer of a DQN corresponds to...

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Bibliographic Details
Main Author:	Sabine Haberland (22783613) (author)
Other Authors:	Hannes Ruge (5815964) (author), Holger Frimmel (22783616) (author)
Published:	2025
Subjects:	Sociology Science Policy Mental Health Biological Sciences not elsewhere classified xlink "> humans significant performance improvements remained unclear whether recorded motor responses humans transform high appropriate motor responses conventional experimental approach human motor responses playing arcade games grained temporal scale human behavior across dqn &# 8217 continuous visual stimuli rl ), enable compare prediction accuracy modeling human behavior third baseline dqn model human behavior continuous experimental tasks deep learning used human behavior experimental tasks prediction accuracy deep rl baseline dqn human participants human data used features three games temporal resolution improved modeling everyday tasks deep q better prediction varying degrees trial structure thereby opening term memory results suggest response probabilities reinforcement learning long short linear model interesting avenue future research dqns ), double q directed actions considerably improved chance level
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DQN-based linear model.

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