Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Safe Reinforcement Learning for Autonomous Vehicles through Parallel
  Constrained Policy Optimization   Reinforcement learning (RL) is attracting increasing interests in autonomous
driving due to its potential to solve complex classification and control
problems. However, existing RL algorithms are rarely applied to real vehicles
for two predominant problems: behaviours are unexplainable, and they cannot
guarantee safety under new scenarios. This paper presents a safe RL algorithm,
called Parallel Constrained Policy Optimization (PCPO), for two autonomous
driving tasks. PCPO extends today's common actor-critic architecture to a
three-component learning framework, in which three neural networks are used to
approximate the policy function, value function and a newly added risk
function, respectively. Meanwhile, a trust region constraint is added to allow
large update steps without breaking the monotonic improvement condition. To
ensure the feasibility of safety constrained problems, synchronized parallel
learners are employed to explore different state spaces, which accelerates
learning and policy-update. The simulations of two scenarios for autonomous
vehicles confirm we can ensure safety while achieving fast learning.

20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics