Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalizing Skills with Semi-Supervised Reinforcement Learning   Deep reinforcement learning (RL) can acquire complex behaviors from low-level
inputs, such as images. However, real-world applications of such methods
require generalizing to the vast variability of the real world. Deep networks
are known to achieve remarkable generalization when provided with massive
amounts of labeled data, but can we provide this breadth of experience to an RL
agent, such as a robot? The robot might continuously learn as it explores the
world around it, even while deployed. However, this learning requires access to
a reward function, which is often hard to measure in real-world domains, where
the reward could depend on, for example, unknown positions of objects or the
emotional state of the user. Conversely, it is often quite practical to provide
the agent with reward functions in a limited set of situations, such as when a
human supervisor is present or in a controlled setting. Can we make use of this
limited supervision, and still benefit from the breadth of experience an agent
might collect on its own? In this paper, we formalize this problem as
semisupervised reinforcement learning, where the reward function can only be
evaluated in a set of "labeled" MDPs, and the agent must generalize its
behavior to the wide range of states it might encounter in a set of "unlabeled"
MDPs, by using experience from both settings. Our proposed method infers the
task objective in the unlabeled MDPs through an algorithm that resembles
inverse RL, using the agent's own prior experience in the labeled MDPs as a
kind of demonstration of optimal behavior. We evaluate our method on
challenging tasks that require control directly from images, and show that our
approach can improve the generalization of a learned deep neural network policy
by using experience for which no reward function is available. We also show
that our method outperforms direct supervised learning of the reward.

2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg