Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Imitation with Neural Density Models   We propose a new framework for Imitation Learning (IL) via density estimation
of the expert's occupancy measure followed by Maximum Occupancy Entropy
Reinforcement Learning (RL) using the density as a reward. Our approach
maximizes a non-adversarial model-free RL objective that provably lower bounds
reverse Kullback-Leibler divergence between occupancy measures of the expert
and imitator. We present a practical IL algorithm, Neural Density Imitation
(NDI), which obtains state-of-the-art demonstration efficiency on benchmark
control tasks.

29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph