Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Active Inference for Pixel-Based Discrete Control: Evaluation on
  the Car Racing Problem   Despite the potential of active inference for visual-based control, learning
the model and the preferences (priors) while interacting with the environment
is challenging. Here, we study the performance of a deep active inference
(dAIF) agent on OpenAI's car racing benchmark, where there is no access to the
car's state. The agent learns to encode the world's state from high-dimensional
input through unsupervised representation learning. State inference and control
are learned end-to-end by optimizing the expected free energy. Results show
that our model achieves comparable performance to deep Q-learning. However,
vanilla dAIF does not reach state-of-the-art performance compared to other
world model approaches. Hence, we discuss the current model implementation's
limitations and potential architectures to overcome them.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th