Horizon-Scalable Q-Learning

这个问题是让我把给定的纯文本新闻正文排版成语义化的HTML片段，要按照指定的规则来处理。首先看正文内容，是一段关于HQL方法的学术新闻，没有分节，但需要看是否有适合用小标题的部分？不过原文是连贯的一段，可能不需要h2/h3，不过再看内容，发现有一句提到实验结果，或许可以把前面的背景和后面的实验分成两节？不过先仔细看规则，规则说长文用h2/h3划分小节，那这段正文里，前面讲问题和方法，后面讲实验，或许可以分？不过先看原文的结构：第一句讲离线学习Q函数随任务时长增加变难，接着讲一步TD的问题，多步TD的问题，然后提出HQL方法，接着解释HQL的规则，然后说这个规则是合理的离线校正，最后讲实验结果。那可能可以把前面的背景和方法部分作为一段，实验部分作为另一段？或者看是否有其他分法。不过再看规则，每个自然段落用p包裹，那原文本身是几个自然段落吗？原文的文本是一段，但里面有多个句子，不过可能是一个自然段落？不对，再看用户给的正文，是连续的一段，没有换行。那需要判断是否需要划分小节。比如，第一部分讲问题和现有方法的不足，第二部分讲提出的HQL方法，第三部分讲实验结果？不过原文是连贯的，可能用户希望保持原段落结构，但如果内容较长，用h2/h3划分。比如，原文中，第一句到“no fixed number of steps suits a whole trajectory.”是背景和问题，然后“In this paper, we propose Horizon-Scalable Q-Learning (HQL)...”是提出方法，然后“Experiments show that...”是实验结果。那或许可以用h2划分“背景与问题”“方法描述”“实验结果”？不过规则说不得增删改内容，所以标题不能用原文没有的内容，那可能原文中没有分节，所以不需要h2/h3？不对，再看规则，长文用h2/h3划分小节，那如果原文是长文，需要划分，但这里原文是一段，可能不算长文？或者看内容，有多个层次，比如问题、方法、实验，这三个部分，可以用h2来分，每个h2下面用p。不过需要注意，标题的内容要来自原文吗？比如，原文中没有明确的小节标题，所以可能不需要加h2，直接用p？但规则说长文用h2/h3划分小节，那可能需要。比如，第一部分讲背景和问题，可以用h2，内容是前面的句子；第二部分讲方法，用h2，内容是HQL的部分；第三部分讲实验，用h2。不过需要确保标题的内容是原文中的意思，不能自己加。比如，原文中“Learning accurate off-policy state-action value functions (Q-functions) from offline datasets becomes harder as the task horizon grows. One-step temporal-difference (TD) learning bootstraps after every transition, so small per-step errors accumulate along the horizon, and early states can be badly misvalued even when the TD loss is small. Multi-step TD shortens this chain by following the dataset trajectory for several steps; however, since the behavior policy is often suboptimal, every followed action adds a bias, and no fixed number of steps suits a whole trajectory.”这部分是背景和问题，可以用h2，比如“背景与问题”？但规则说不得增删改内容，所以标题不能用原文没有的词，那可能原文中没有这样的标题，所以不能加？那可能不需要h2/h3，直接用p？不过再看规则，长文用h2/h3划分小节，那如果原文是一段，可能不需要。那现在看原文的内容，是否有列表、引用、代码？没有，所以主要用p。不过看原文中的关键句子，比如“we propose Horizon-Scalable Q-Learning (HQL), a simple method that enables accurate long-horizon value learning by automatically balancing between one-step and multi-step returns.”可以用strong标注。还有“this rule is a principled off-policy correction that needs no action densities”也可以用strong。那现在开始排版：首先，原文的第一部分，从开头到“no fixed number of steps suits a whole trajectory.”作为第一个p？然后“In this paper, we propose Horizon-Scalable Q-Learning (HQL)... falls back to a one-step target.”作为第二个p？然后“We show that this rule is a principled off-policy correction... makes HQL compatible with expressive flow and diffusion policies.”作为第三个p？然后“Experiments show that...”作为第四个p？不过需要确认每个自然段落。原文是一个自然段落，但为了语义化，可能需要分成几个p。或者保持原文的自然段落，原文是一个自然段落，所以用一个p？但可能用户希望分成多个p。不过规则说每个自然段落用p包裹，所以如果原文是一个自然段落，就用一个p。但再看规则，长文用h2/h3划分小节，所以可能需要划分。比如，把原文分成三个部分：问题、方法、实验，每个部分用h2，下面用p。比如：

背景与问题

Learning accurate off-policy state-action value functions (Q-functions) from offline datasets becomes harder as the task horizon grows. One-step temporal-difference (TD) learning bootstraps after every transition, so small per-step errors accumulate along the horizon, and early states can be badly misvalued even when the TD loss is small. Multi-step TD shortens this chain by following the dataset trajectory for several steps; however, since the behavior policy is often suboptimal, every followed action adds a bias, and no fixed number of steps suits a whole trajectory.

方法：HQL

In this paper, we propose Horizon-Scalable Q-Learning (HQL), a simple method that enables accurate long-horizon value learning by automatically balancing between one-step and multi-step returns. HQL extends a target through a dataset action only when the critic values it at least as highly as the best-of-N actions sampled from the BC policy, and otherwise falls back to a one-step target.

实验结果

We show that this rule is a principled off-policy correction that needs no action densities, which makes HQL compatible with expressive flow and diffusion policies. Experiments show that HQL avoids the failures of both one-step and multi-step TD on a combination-lock task across horizons, and outperforms prior horizon-reduction methods on long-horizon OGBench and OCBench tasks.

不过这里的h2标题是“背景与问题”“方法：HQL”“实验结果”，但原文中没有这些标题，规则说不得增删改内容，所以这样加标题是否算增删？哦，规则说保留原文所有信息，不得增删改内容，所以不能添加原文没有的内容，所以h2的标题不能用自己加的词，那可能h2的内容要用原文中的句子？比如，用“Learning accurate off-policy state-action value functions (Q-functions) from offline datasets becomes harder as the task horizon grows.”作为h2？但这样不太合适。或者，原文中没有分节，所以不需要h2/h3，直接用p。那可能正确的做法是把原文作为一个自然段落，用p包裹，但里面的关键句子用strong。比如：

Learning accurate off-policy state-action value functions (Q-functions) from offline datasets becomes harder as the task horizon grows. One-step temporal-difference (TD) learning bootstraps after every transition, so small per-step errors accumulate along the horizon, and early states can be badly misvalued even when the TD loss is small. Multi-step TD shortens this chain by following the dataset trajectory for several steps; however, since the behavior policy is often suboptimal, every followed action adds a bias, and no fixed number of steps suits a whole trajectory. In this paper, we propose Horizon-Scalable Q-Learning (HQL), a simple method that enables accurate long-horizon value learning by automatically balancing between one-step and multi-step returns. HQL extends a target through a dataset action only when the critic values it at least as highly as the best-of-N actions sampled from the BC policy, and otherwise falls back to a one-step target. We show that this rule is a principled off-policy correction that needs no action densities, which makes HQL compatible with expressive flow and diffusion policies. Experiments show that HQL avoids the failures of both one-step and multi-step TD on a combination-lock task across horizons, and outperforms prior horizon-reduction methods on long-horizon OGBench and OCBench tasks.

这样是否符合规则？规则说重要句子可用strong，所以把提出方法和关键结论用strong。然后整个作为一个p，因为原文是一个自然段落。不过再看规则，长文用h2/h3