Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transient Non-Stationarity and Generalisation in Deep Reinforcement
  Learning   Non-stationarity can arise in Reinforcement Learning (RL) even in stationary
environments. For example, most RL algorithms collect new data throughout
training, using a non-stationary behaviour policy. Due to the transience of
this non-stationarity, it is often not explicitly addressed in deep RL and a
single neural network is continually updated. However, we find evidence that
neural networks exhibit a memory effect where these transient
non-stationarities can permanently impact the latent representation and
adversely affect generalisation performance. Consequently, to improve
generalisation of deep RL agents, we propose Iterated Relearning (ITER). ITER
augments standard RL training by repeated knowledge transfer of the current
policy into a freshly initialised network, which thereby experiences less
non-stationarity during training. Experimentally, we show that ITER improves
performance on the challenging generalisation benchmarks ProcGen and Multiroom.

13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an