Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A New Framework for Query Efficient Active Imitation Learning   We seek to align agent policy with human expert behavior in a reinforcement
learning (RL) setting, without any prior knowledge about dynamics, reward
function, and unsafe states. There is a human expert knowing the rewards and
unsafe states based on his preference and objective, but querying that human
expert is expensive. To address this challenge, we propose a new framework for
imitation learning (IL) algorithm that actively and interactively learns a
model of the user's reward function with efficient queries. We build an
adversarial generative model of states and a successor feature (SR) model
trained over transition experience collected by learning policy. Our method
uses these models to select state-action pairs, asking the user to comment on
the optimality or safety, and trains a adversarial neural network to predict
the rewards. Different from previous papers, which are almost all based on
uncertainty sampling, the key idea is to actively and efficiently select
state-action pairs from both on-policy and off-policy experience, by
discriminating the queried (expert) and unqueried (generated) data and
maximizing the efficiency of value function learning. We call this method
adversarial reward query with successor representation. We evaluate the
proposed method with simulated human on a state-based 2D navigation task,
robotic control tasks and the image-based video games, which have
high-dimensional observation and complex state dynamics. The results show that
the proposed method significantly outperforms uncertainty-based methods on
learning reward models, achieving better query efficiency, where the
adversarial discriminator can make the agent learn human behavior more
efficiently and the SR can select states which have stronger impact on value
function. Moreover, the proposed method can also learn to avoid unsafe states
when training the reward model.

34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
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The categories of this paper are: solv-int
30
The categories of this paper are: q-alg