Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Reliability and Generalizability of Brain-inspired Reinforcement
  Learning Algorithms   Although deep RL models have shown a great potential for solving various
types of tasks with minimal supervision, several key challenges remain in terms
of learning from limited experience, adapting to environmental changes, and
generalizing learning from a single task. Recent evidence in decision
neuroscience has shown that the human brain has an innate capacity to resolve
these issues, leading to optimism regarding the development of
neuroscience-inspired solutions toward sample-efficient, and generalizable RL
algorithms. We show that the computational model combining model-based and
model-free control, which we term the prefrontal RL, reliably encodes the
information of high-level policy that humans learned, and this model can
generalize the learned policy to a wide range of tasks. First, we trained the
prefrontal RL, and deep RL algorithms on 82 subjects' data, collected while
human participants were performing two-stage Markov decision tasks, in which we
manipulated the goal, state-transition uncertainty and state-space complexity.
In the reliability test, which includes the latent behavior profile and the
parameter recoverability test, we showed that the prefrontal RL reliably
learned the latent policies of the humans, while all the other models failed.
Second, to test the ability to generalize what these models learned from the
original task, we situated them in the context of environmental volatility.
Specifically, we ran large-scale simulations with 10 Markov decision tasks, in
which latent context variables change over time. Our information-theoretic
analysis showed that the prefrontal RL showed the highest level of adaptability
and episodic encoding efficacy. This is the first attempt to formally test the
possibility that computational models mimicking the way the brain solves
general problems can lead to practical solutions to key challenges in machine
learning.

32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn