DQN-based linear model.

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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