General policies represent reactive strategies for solving large families of
planning problems like the infinite collection of solvable instances from a
given domain. Methods for learning such policies from a collection of small
training instances have been developed successfully for classical domains. In
this work, we extend the formulations and the resulting combinatorial methods
for learning general policies over fully observable, non-deterministic (FOND)
domains. We also evaluate the resulting approach experimentally over a number
of benchmark domains in FOND planning, present the general policies that result
in some of these domains, and prove their correctness. The method for learning
general policies for FOND planning can actually be seen as an alternative FOND
planning method that searches for solutions, not in the given state space but
in an abstract space defined by features that must be learned as well.

扩展学习通用策略的公式和组合方法以解决完全可观察、非确定性（FOND）领域的规划问题，通过实验证实所得方法在多个 FOND 规划基准领域上，并验证了其正确性。学习 FOND 规划的通用策略方法可以被视为在抽象空间中寻找解决方案的一种替代 FOND 规划方法，该抽象空间由需要学习的特征定义。

学习完全可观察的非确定性计划领域的广义策略

Learning Generalized Policies for Fully Observable Non-Deterministic  Planning Domains

It has been observed that in many of the benchmark planning domains, atomic
goals can be reached with a simple polynomial exploration procedure, called IW,
that runs in time exponential in the problem width. Such problems have indeed a
bounded width: a width that does not grow with the number of problem variables
and is often no greater than two. Yet, while the notion of width has become
part of the state-of-the-art planning algorithms like BFWS, there is still no
good explanation for why so many benchmark domains have bounded width. In this
work, we address this question by relating bounded width and serialized width
to ideas of generalized planning, where general policies aim to solve multiple
instances of a planning problem all at once. We show that bounded width is a
property of planning domains that admit optimal general policies in terms of
features that are explicitly or implicitly represented in the domain encoding.
The results are extended to much larger class of domains with bounded
serialized width where the general policies do not have to be optimal. The
study leads also to a new simple, meaningful, and expressive language for
specifying domain serializations in the form of policy sketches which can be
used for encoding domain control knowledge by hand or for learning it from
traces. The use of sketches and the meaning of the theoretical results are all
illustrated through a number of examples.

研究结论指出，bounded width 是规划域类的一个特性，它可以被用来表示这些域具有一般策略的最优解。这些规划域的表示形式（聚合信息）可以被简单，有意义和有表现力的计划草图语言表示。

一般政策、序列化和计划宽度

General Policies, Serializations, and Planning Width

We study the characterization and computation of general policies for
families of problems that share a structure characterized by a common reduction
into a single abstract problem. Policies $\mu$ that solve the abstract problem
P have been shown to solve all problems Q that reduce to P provided that $\mu$
terminates in Q. In this work, we shed light on why this termination condition
is needed and how it can be removed. The key observation is that the abstract
problem P captures the common structure among the concrete problems Q that is
local (Markovian) but misses common structure that is global. We show how such
global structure can be captured by means of trajectory constraints that in
many cases can be expressed as LTL formulas, thus reducing generalized planning
to LTL synthesis. Moreover, for a broad class of problems that involve integer
variables that can be increased or decreased, trajectory constraints can be
compiled away, reducing generalized planning to fully observable
non-deterministic planning.

本文研究了具有共同特征的问题集合的一般策略的特征和计算，并介绍了如何通过利用轨迹约束以及 LTL 公式来消除策略的终止条件以及降低规划的复杂度。