Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Depth and nonlinearity induce implicit exploration for RL   The question of how to explore, i.e., take actions with uncertain outcomes to
learn about possible future rewards, is a key question in reinforcement
learning (RL). Here, we show a surprising result: We show that Q-learning with
nonlinear Q-function and no explicit exploration (i.e., a purely greedy policy)
can learn several standard benchmark tasks, including mountain car, equally
well as, or better than, the most commonly-used $\epsilon$-greedy exploration.
We carefully examine this result and show that both the depth of the Q-network
and the type of nonlinearity are important to induce such deterministic
exploration.

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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an