Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SEED RL: Scalable and Efficient Deep-RL with Accelerated Central
  Inference   We present a modern scalable reinforcement learning agent called SEED
(Scalable, Efficient Deep-RL). By effectively utilizing modern accelerators, we
show that it is not only possible to train on millions of frames per second but
also to lower the cost of experiments compared to current methods. We achieve
this with a simple architecture that features centralized inference and an
optimized communication layer. SEED adopts two state of the art distributed
algorithms, IMPALA/V-trace (policy gradients) and R2D2 (Q-learning), and is
evaluated on Atari-57, DeepMind Lab and Google Research Football. We improve
the state of the art on Football and are able to reach state of the art on
Atari-57 three times faster in wall-time. For the scenarios we consider, a 40%
to 80% cost reduction for running experiments is achieved. The implementation
along with experiments is open-sourced so results can be reproduced and novel
ideas tried out.

27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg