Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributional Reinforcement Learning for Efficient Exploration   In distributional reinforcement learning (RL), the estimated distribution of
value function models both the parametric and intrinsic uncertainties. We
propose a novel and efficient exploration method for deep RL that has two
components. The first is a decaying schedule to suppress the intrinsic
uncertainty. The second is an exploration bonus calculated from the upper
quantiles of the learned distribution. In Atari 2600 games, our method
outperforms QR-DQN in 12 out of 14 hard games (achieving 483 \% average gain
across 49 games in cumulative rewards over QR-DQN with a big win in Venture).
We also compared our algorithm with QR-DQN in a challenging 3D driving
simulator (CARLA). Results show that our algorithm achieves near-optimal safety
rewards twice faster than QRDQN.

30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an