Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Replacing Rewards with Examples: Example-Based Policy Search via
  Recursive Classification   Reinforcement learning (RL) algorithms assume that users specify tasks by
manually writing down a reward function. However, this process can be laborious
and demands considerable technical expertise. Can we devise RL algorithms that
instead enable users to specify tasks simply by providing examples of
successful outcomes? In this paper, we derive a control algorithm that
maximizes the future probability of these successful outcome examples. Prior
work has approached similar problems with a two-stage process, first learning a
reward function and then optimizing this reward function using another RL
algorithm. In contrast, our method directly learns a value function from
transitions and successful outcomes, without learning this intermediate reward
function. Our method therefore requires fewer hyperparameters to tune and lines
of code to debug. We show that our method satisfies a new data-driven Bellman
equation, where examples take the place of the typical reward function term.
Experiments show that our approach outperforms prior methods that learn
explicit reward functions.

35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
16
The categories of this paper are: stat