Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Agents using Upside-Down Reinforcement Learning   We develop Upside-Down Reinforcement Learning (UDRL), a method for learning
to act using only supervised learning techniques. Unlike traditional
algorithms, UDRL does not use reward prediction or search for an optimal
policy. Instead, it trains agents to follow commands such as "obtain so much
total reward in so much time." Many of its general principles are outlined in a
companion report; the goal of this paper is to develop a practical learning
algorithm and show that this conceptually simple perspective on agent training
can produce a range of rewarding behaviors for multiple episodic environments.
Experiments show that on some tasks UDRL's performance can be surprisingly
competitive with, and even exceed that of some traditional baseline algorithms
developed over decades of research. Based on these results, we suggest that
alternative approaches to expected reward maximization have an important role
to play in training useful autonomous agents.

3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol