Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Procedural Content Generation: Better Benchmarks for Transfer
  Reinforcement Learning   The idea of transfer in reinforcement learning (TRL) is intriguing: being
able to transfer knowledge from one problem to another problem without learning
everything from scratch. This promises quicker learning and learning more
complex methods. To gain an insight into the field and to detect emerging
trends, we performed a database search. We note a surprisingly late adoption of
deep learning that starts in 2018. The introduction of deep learning has not
yet solved the greatest challenge of TRL: generalization. Transfer between
different domains works well when domains have strong similarities (e.g.
MountainCar to Cartpole), and most TRL publications focus on different tasks
within the same domain that have few differences. Most TRL applications we
encountered compare their improvements against self-defined baselines, and the
field is still missing unified benchmarks. We consider this to be a
disappointing situation. For the future, we note that: (1) A clear measure of
task similarity is needed. (2) Generalization needs to improve. Promising
approaches merge deep learning with planning via MCTS or introduce memory
through LSTMs. (3) The lack of benchmarking tools will be remedied to enable
meaningful comparison and measure progress. Already Alchemy and Meta-World are
emerging as interesting benchmark suites. We note that another development, the
increase in procedural content generation (PCG), can improve both benchmarking
and generalization in TRL.

23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph