Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Formal Policy Synthesis for Continuous-Space Systems via Reinforcement
  Learning   This paper studies satisfaction of temporal properties on unknown stochastic
processes that have continuous state spaces. We show how reinforcement learning
(RL) can be applied for computing policies that are finite-memory and
deterministic using only the paths of the stochastic process. We address
properties expressed in linear temporal logic (LTL) and use their automaton
representation to give a path-dependent reward function maximised via the RL
algorithm. We develop the required assumptions and theories for the convergence
of the learned policy to the optimal policy in the continuous state space. To
improve the performance of the learning on the constructed sparse reward
function, we propose a sequential learning procedure based on a sequence of
labelling functions obtained from the positive normal form of the LTL
specification. We use this procedure to guide the RL algorithm towards a policy
that converges to an optimal policy under suitable assumptions on the process.
We demonstrate the approach on a 4-dim cart-pole system and 6-dim boat driving
problem.

37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph