Autonomous driving technology has five levels, from L0 to L5. Currently, only
the L2 level (partial automation) can be achieved, and there is a long way to
go before reaching the final level of L5 (full automation). The key to crossing
these levels lies in training the autonomous driving model. However, relying
solely on real-world road data to train the model is far from enough and
consumes a great deal of resources. Although there are already examples of
training autonomous driving models through simulators that simulate real-world
scenarios, these scenarios require complete manual construction. Directly
converting 3D scenes from road network formats will lack a large amount of
detail and cannot be used as training sets. Underground parking garage static
scenario simulation is regarded as a procedural content generation (PCG)
problem. This paper will use the Sarsa algorithm to solve procedural content
generation on underground garage structures.

自动驾驶技术包括五个级别，从 L0 到 L5。当前只能实现 L2 级别（部分自动化），达到最终的 L5 级别（全自动化）还有很长的路要走。跨越这些级别的关键在于训练自动驾驶模型。然而，仅依靠实际道路数据训练模型远远不够且消耗大量资源。尽管已经有通过模拟器训练自动驾驶模型的例子，但这些场景需要完全手动构建。直接从道路网络格式转换 3D 场景将缺乏大量细节，无法作为训练集使用。地下停车车库静态场景模拟被视为程序化内容生成（PCG）问题。本文将使用 Sarsa 算法解决地下车库结构的程序化内容生成。

基于 PCG 的静态地下车库场景生成

PCG-based Static Underground Garage Scenario Generation

The three most common representations of states in iterated belief revision
are compared: explicit, by levels and by history. The first is a connected
preorder between models, the second is a list of formulae representing
equivalence classes, the third is the sequence of the previous revisions. The
latter depends on the revision semantics and on history rewriting, and the
latter depends on the allowed rewritings. All mechanisms represent all possible
states. A rewritten history of lexicographic revision is more efficient than
the other considered representations in terms of size with arbitrary history
rewritings. Establishing the redundancy of such a history is a mild rewriting.
It is coNP-complete in the general case, and is hard even on histories of two
revisions or revisions of arbitrary length of Horn formulae, and is polynomial
on histories of two Horn formulae. A minor technical result is a
polynomial-time algorithm for establishing whether a Horn formula is equivalent
to the negation of another Horn formula.

比较了置信修正中三种最常见的状态表示方式：显式表示、按级别表示和按历史记录表示的差异，并研究了它们之间的联系与区别。