Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Universal Successor Representations for Transfer Reinforcement Learning   The objective of transfer reinforcement learning is to generalize from a set
of previous tasks to unseen new tasks. In this work, we focus on the transfer
scenario where the dynamics among tasks are the same, but their goals differ.
Although general value function (Sutton et al., 2011) has been shown to be
useful for knowledge transfer, learning a universal value function can be
challenging in practice. To attack this, we propose (1) to use universal
successor representations (USR) to represent the transferable knowledge and (2)
a USR approximator (USRA) that can be trained by interacting with the
environment. Our experiments show that USR can be effectively applied to new
tasks, and the agent initialized by the trained USRA can achieve the goal
considerably faster than random initialization.

26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
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
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph