Training results for the grid world environment.

Training results for the grid world environment.

<p>a) Evolution of the length of the trajectories during the training, for different scaling parameters ranging from −3 to 3, and different preference distributions: the agent can either learn to complete the task from the start (“task”), or first explore the grid (“explore”). We represent the...

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Bibliographic Details
Main Author: Joséphine Pazem (22184363) (author)
Other Authors: Marius Krumm (22184366) (author), Alexander Q. Vining (11320591) (author), Lukas J. Fiderer (4865587) (author), Hans J. Briegel (6383642) (author)
Published: 2025
Subjects:
Science Policy
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
using internal rewards
partially observable grid
free energy principle
expected free energy
agents &# 8217
timed response task
various reinforcement learning
partially observable environments
feps agents build
interpretability </ p
build upon
model agents
navigation task
given task
feps ).
feps model
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understanding aspects
term goals
target observation
results show
recent work
prediction accuracy
observations based
multidisciplinary interest
mathematical models
last decade
including elements
constraints imposed
complex environments
behavioral biology
appropriately contextualizing
active inference
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