Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement
  Learning   Model-based reinforcement learning algorithms make decisions by building and
utilizing a model of the environment. However, none of the existing algorithms
attempts to infer the dynamics of any state-action pair from known state-action
pairs before meeting it for sufficient times. We propose a new model-based
method called Greedy Inference Model (GIM) that infers the unknown dynamics
from known dynamics based on the internal spectral properties of the
environment. In other words, GIM can "learn by analogy". We further introduce a
new exploration strategy which ensures that the agent rapidly and evenly visits
unknown state-action pairs. GIM is much more computationally efficient than
state-of-the-art model-based algorithms, as the number of dynamic programming
operations is independent of the environment size. Lower sample complexity
could also be achieved under mild conditions compared against methods without
inferring. Experimental results demonstrate the effectiveness and efficiency of
GIM in a variety of real-world tasks.

31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn