解决语言指令模糊问题，让智能体更准确理解人类指令。
DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning
- 引入分布对齐学习机制，提升语言任务的可区分性。
- 在多个基准上优于基线方法，显著降低指令歧义影响。
- 适合需要精准理解自然语言指令的强化学习场景。
理解和遵循自然语言指令是智能体的关键能力。然而，语言指令的灵活性导致语言条件任务中存在严重歧义，严重损害算法性能。为此，我们提出一种新方法DAIL（分布对齐学习），包含两个核心组件：分布策略与语义对齐。理论上证明，价值分布估计机制增强了任务可区分性；语义对齐模块则捕捉轨迹与语言指令间的对应关系。在结构化与视觉观测基准上的大量实验表明，DAIL能有效缓解指令歧义，性能显著优于基线方法。代码已开源：https://github.com/RunpengXie/Distributional-Aligned-Learning。
原文摘要 · Abstract (English)
Comprehending natural language and following human instructions are critical capabilities for intelligent agents. However, the flexibility of linguistic instructions induces substantial ambiguity across language-conditioned tasks, severely degrading algorithmic performance. To address these limitations, we present a novel method named DAIL (Distributional Aligned Learning), featuring two key components: distributional policy and semantic alignment. Specifically, we provide theoretical results that the value distribution estimation mechanism enhances task differentiability. Meanwhile, the semantic alignment module captures the correspondence between trajectories and linguistic instructions. Extensive experimental results on both structured and visual observation benchmarks demonstrate that DAIL effectively resolves instruction ambiguities, achieving superior performance to baseline methods. Our implementation is available at https://github.com/RunpengXie/Distributional-Aligned-Learning.
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