Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use
This study enhances offline RL agents using diverse, informative language feedback, achieving over 10% performance gains on benchmarks.
Key Findings
Methodology
The paper extends the Decision Transformer (DT) into a multi-modal Language-Teachable Decision Transformer (LTDT), integrating rich language feedback generated via GPT-4. It constructs a diverse language pool with templates and GPT augmentation, enabling the generation of varied, informative instructions. The framework incorporates hindsight and foresight feedback, reflecting past mistakes and future guidance, to improve task understanding. Data collection involves expert and sub-optimal policies to promote exploration. The training optimizes sequence prediction of actions conditioned on language, environment states, and rewards, using cross-entropy or MSE loss. Experiments evaluate performance across four offline RL benchmarks, comparing models trained with different levels of language richness and diversity.
Key Results
- Agents trained with diverse, GPT-augmented language feedback outperform baselines by over 10 percentage points (from 37.95% to 57.95%) across four environments, demonstrating the effectiveness of rich language in improving task success and generalization. Incorporating both hindsight and foresight feedback yields the highest gains, especially in unseen tasks, with performance improvements exceeding 15%. The results confirm that language informativeness and diversity significantly enhance the agent’s ability to learn and adapt.
- In ablation studies, models with GPT-enhanced instructions outperform those with only handcrafted templates, highlighting the importance of linguistic diversity. The combination of feedback types (hindsight + foresight) consistently yields better results than either alone. The experiments also show that models trained with richer language generalize better in few-shot adaptation scenarios, indicating stronger transfer capabilities.
- Performance metrics include task completion reward, with models trained on 10,000+ trajectories. Results are averaged over multiple seeds, confirming robustness. The models excel particularly in zero-shot and few-shot settings, validating the approach’s potential for real-world applications where data is limited.
Significance
This work underscores the critical role of natural language in advancing embodied AI, moving beyond simple commands to rich, expressive communication. It addresses longstanding challenges in task generalization and transfer learning, providing a scalable framework for integrating complex human-like instructions into reinforcement learning. The findings have broad implications for human-robot interaction, autonomous systems, and AI-assisted decision-making, paving the way for more intuitive and adaptable intelligent agents. By demonstrating that language diversity and informativeness directly impact learning efficiency, the study offers a new paradigm for designing AI systems capable of natural, flexible communication.
Technical Contribution
The paper introduces a novel multi-modal transformer architecture (LTDT) that seamlessly incorporates language feedback into RL training. It innovates by leveraging GPT-4 to generate diverse, natural language instructions, and combines hindsight and foresight feedback to enrich training signals. The approach systematically evaluates the impact of language richness on offline RL performance, providing empirical evidence of performance gains. The framework enables scalable, data-driven generation of instructions without human annotation, significantly advancing the state-of-the-art in language-guided reinforcement learning. The integration of language embeddings from Sentence-BERT further enhances semantic sensitivity, improving model robustness.
Novelty
This is the first comprehensive study systematically analyzing how language informativeness and diversity influence offline RL agents. It uniquely combines GPT-augmented instruction generation with a dual feedback mechanism (hindsight and foresight), demonstrating substantial performance improvements over traditional low-level commands. The work bridges the gap between NLP and RL, establishing a scalable, data-efficient framework that mimics human-like communication in AI training. Its innovative architecture and empirical validation set new benchmarks for language-guided embodied learning.
Limitations
- The approach relies heavily on large pre-trained language models like GPT-4, which entail high computational costs, limiting real-time deployment.
- Experiments are conducted in simulated environments; transferring these results to real-world robotics involves additional challenges such as noise, dynamic changes, and safety considerations.
- Instruction generation depends on templates and GPT augmentation, which may not fully capture the richness of human language, potentially limiting naturalness and diversity in complex scenarios.
Future Work
Future research will explore online, interactive feedback mechanisms to adapt instructions dynamically during task execution. Integrating multimodal sensory data, such as vision and touch, can further improve understanding. Developing more efficient instruction generation methods and reducing dependency on large language models will be key. Additionally, extending the framework to real-world robotic platforms and multi-agent systems will be pursued to enhance practical applicability and robustness.
AI Executive Summary
This research tackles a fundamental challenge in embodied AI: how to leverage natural language to improve task learning and generalization in reinforcement learning agents. Traditional RL approaches rely heavily on numerical rewards, which often lack the richness needed for complex, real-world tasks. To address this, the authors propose a novel framework that incorporates diverse, informative language feedback into the training process. Extending the Decision Transformer architecture, they develop the Language-Teachable Decision Transformer (LTDT), which integrates rich language signals generated via GPT-4. By constructing a GPT-augmented language pool, the method produces varied instructions that include both retrospective (hindsight) and prospective (foresight) guidance, mimicking human-like communication.
Experimental validation across four benchmark environments—HomeGrid, ALFWorld, Messenger, and MetaWorld—demonstrates that models trained with these rich language signals outperform baseline models by over 10 percentage points in task success rate. The results highlight that language informativeness and diversity are crucial for improving the agent’s understanding, adaptability, and robustness, especially in unseen tasks. The approach not only advances the theoretical understanding of language’s role in reinforcement learning but also offers practical pathways for deploying more natural, flexible AI agents.
Despite these successes, the reliance on large pre-trained models and simulated environments presents limitations. Future work aims to optimize efficiency, extend to real-world robotics, and incorporate online, interactive feedback. Overall, this work marks a significant step toward more human-like, communicative AI systems capable of learning in open, complex environments, with broad implications for robotics, automation, and human-AI collaboration.
Deep Analysis
Background
近年来，强化学习（RL）在自主决策、机器人控制等领域取得显著进展，但其对奖励信号的依赖限制了在复杂环境中的泛化能力。传统方法多采用数值奖励，难以充分利用人类丰富的语言信息。随着大规模预训练模型（如GPT、BERT）的兴起，研究开始探索将自然语言引入RL，旨在提升智能体的理解和交互能力。早期工作如She和Chai（2017）使用简单指令引导机器人，后续研究逐渐引入复杂对话、多模态融合（Radford et al., 2021; Dai et al., 2023），但大多仍局限于低级指令，缺乏系统性分析语言丰富性与多样性的作用。当前，学界关注如何利用人类自然沟通的复杂表达，推动智能体在开放环境中的泛化。
Core Problem
核心问题在于，现有强化学习方法多依赖简单、单一的指令，难以反映人类沟通的丰富性和多样性。这限制了智能体在复杂、多变环境中的任务迁移和泛化能力。具体挑战包括：如何设计信息丰富的指令、如何确保指令的多样性、以及如何融合自然语言与RL模型。传统指令缺乏对任务细节的表达，导致模型理解有限，难以应对实际应用中的复杂场景。解决这些问题，将极大推动自主系统的智能化水平。
Innovation
本研究的创新点主要包括：1）提出结合GPT增强的多样化语言池，丰富指令表达；2）引入回顾（hindsight）与前瞻（foresight）机制，提升指令信息量；3）扩展决策变换器（DT）为多模态LTDT模型，有效融合语言与动作信息；4）系统验证丰富、多样的语言反馈对性能的提升，提供理论和工程上的新路径。这些创新突破了传统低级指令的限制，为强化学习引入更接近人类自然沟通的交互方式。
Methodology
- �� 构建离线数据集：采集专家和非专家策略生成的多样化轨迹，结合模板和GPT增强技术生成丰富指令。• 设计回顾（hindsight）和前瞻（foresight）语言模板，反映过去行为和未来指导。• 利用GPT-4生成多样表达，将指令存入语言池，随机采样用于训练。• 扩展DT模型为LTDT，将语言反馈作为输入，增强模型理解能力。• 训练过程中，采样轨迹子序列，预测下一动作，优化交叉熵或MSE损失。• 使用Sentence-BERT编码语言信息，确保语义敏感。• 在四个环境中进行大规模实验，评估不同指令丰富性和多样性对性能的影响。
Experiments
实验在HomeGrid、ALFWorld、Messenger和MetaWorld四个离线RL环境中进行，采用不同模板和GPT增强指令池，比较无语言、单一模板、丰富指令的性能差异。模型训练后评估任务完成率，测试已见和未见任务的迁移能力。超参数如轨迹数（100-20000）和微调样本（5-20）经过调优，结果由多次随机种子平均确保稳健。对比不同指令类型的效果，验证信息丰富性和表达多样性的重要性。
Results
丰富且多样的语言反馈显著提升模型性能，平均提升超过10个百分点。在未见任务上，预训练模型使用GPT增强指令实现更快适应，性能提升达15%以上。结合回顾与前瞻信息的指令效果优于单一类型，验证了信息丰富性的重要性。ablation分析显示，单纯模板指令效果较差，GPT增强显著改善了指令多样性和理解能力。这些结果表明，丰富的语言表达是提升泛化能力的关键。
Applications
该方法适用于自主机器人、智能家居、工业自动化等场景，提升系统在复杂环境中的自主学习和适应能力。引入自然语言交互，用户无需编程即可指导机器人完成多样任务，降低使用门槛。未来结合在线学习和人类反馈，将实现更智能、更自然的人机协作，推动智能系统的广泛应用。
Limitations & Outlook
模型对大规模预训练语言模型依赖较高，计算成本昂贵，实际部署需优化效率。环境模拟简化，难以完全反映真实世界复杂性，存在迁移难题。指令生成主要依赖模板和GPT增强，表达仍有限，未来需探索更自然的交互方式。此外，模型在极端复杂任务中的表现仍需提升。
Plain Language Accessible to non-experts
想象你在教一个朋友玩游戏。以前，你只说“跳”或“跑”，他可能不太明白。现在，你用更有趣的话，比如“快点跳过那个障碍”或者“跑到那边去”，而且用不同的说法，比如“跳过它”或“快跑到那边”。这样，他就更容易理解，也更快学会怎么玩。这个研究就像这样：用丰富多彩的语言告诉机器人任务，让它更聪明、更快学会新东西。研究发现，越是用多样、详细的语言，机器人就越能理解任务，也能更好地适应新环境，就像我们学东西一样，听得多、看得多，记得更牢。未来，这样的技术可以让机器人更贴近人类的沟通方式，变得更聪明、更有用。
ELI14 Explained like you're 14
想象你在教你的朋友玩游戏。以前，你只说“跳”或“跑”，但他可能不太明白。现在，你用更有趣的话，比如“快点跳过那个障碍”或者“跑到那边去”，而且用不同的说法，比如“跳过它”或“快跑到那边”。这样，他就更容易理解，也更快学会怎么玩。这个研究就像这样：用丰富多彩的语言告诉机器人任务，让它更聪明、更快学会新东西。研究发现，越是用多样、详细的语言，机器人就越能理解任务，也能更好地适应新环境。未来，这样的技术可以让机器人像我们一样会说话、理解我们，让人机合作变得更自然、更顺畅。
Abstract
In real-world scenarios, it is desirable for embodied agents to have the ability to leverage human language to gain explicit or implicit knowledge for learning tasks. Despite recent progress, most previous approaches adopt simple low-level instructions as language inputs, which may not reflect natural human communication. It's not clear how to incorporate rich language use to facilitate task learning. To address this question, this paper studies different types of language inputs in facilitating reinforcement learning (RL) embodied agents. More specifically, we examine how different levels of language informativeness (i.e., feedback on past behaviors and future guidance) and diversity (i.e., variation of language expressions) impact agent learning and inference. Our empirical results based on four RL benchmarks demonstrate that agents trained with diverse and informative language feedback can achieve enhanced generalization and fast adaptation to new tasks. These findings highlight the pivotal role of language use in teaching embodied agents new tasks in an open world. Project website: https://github.com/sled-group/Teachable_RL