Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Maximum Causal Entropy for Inverse Reinforcement Learning   We consider the problem of learning from demonstrated trajectories with
inverse reinforcement learning (IRL). Motivated by a limitation of the
classical maximum entropy model in capturing the structure of the network of
states, we propose an IRL model based on a generalized version of the causal
entropy maximization problem, which allows us to generate a class of maximum
entropy IRL models. Our generalized model has an advantage of being able to
recover, in addition to a reward function, another expert's function that would
(partially) capture the impact of the connecting structure of the states on
experts' decisions. Empirical evaluation on a real-world dataset and a
grid-world dataset shows that our generalized model outperforms the classical
ones, in terms of recovering reward functions and demonstrated trajectories.

12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph