Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Consciousness-Inspired Planning Agent for Model-Based Reinforcement
  Learning   We present an end-to-end, model-based deep reinforcement learning agent which
dynamically attends to relevant parts of its state during planning. The agent
uses a bottleneck mechanism over a set-based representation to force the number
of entities to which the agent attends at each planning step to be small. In
experiments, we investigate the bottleneck mechanism with several sets of
customized environments featuring different challenges. We consistently observe
that the design allows the planning agents to generalize their learned
task-solving abilities in compatible unseen environments by attending to the
relevant objects, leading to better out-of-distribution generalization
performance.

9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph