Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Safe Exploration in Continuous Action Spaces   We address the problem of deploying a reinforcement learning (RL) agent on a
physical system such as a datacenter cooling unit or robot, where critical
constraints must never be violated. We show how to exploit the typically smooth
dynamics of these systems and enable RL algorithms to never violate constraints
during learning. Our technique is to directly add to the policy a safety layer
that analytically solves an action correction formulation per each state. The
novelty of obtaining an elegant closed-form solution is attained due to a
linearized model, learned on past trajectories consisting of arbitrary actions.
This is to mimic the real-world circumstances where data logs were generated
with a behavior policy that is implausible to describe mathematically; such
cases render the known safety-aware off-policy methods inapplicable. We
demonstrate the efficacy of our approach on new representative physics-based
environments, and prevail where reward shaping fails by maintaining zero
constraint violations.

23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph