Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantifying Generalization in Reinforcement Learning   In this paper, we investigate the problem of overfitting in deep
reinforcement learning. Among the most common benchmarks in RL, it is customary
to use the same environments for both training and testing. This practice
offers relatively little insight into an agent's ability to generalize. We
address this issue by using procedurally generated environments to construct
distinct training and test sets. Most notably, we introduce a new environment
called CoinRun, designed as a benchmark for generalization in RL. Using
CoinRun, we find that agents overfit to surprisingly large training sets. We
then show that deeper convolutional architectures improve generalization, as do
methods traditionally found in supervised learning, including L2
regularization, dropout, data augmentation and batch normalization.

12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics