TEA通过动态编码增强状态空间，提升离线强化学习策略的泛化能力。
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning
- 用序列编码器提取环境动态的潜在表示，扩展状态空间。
- 在未见动态环境中，策略迁移效果优于仅使用原始状态的方法。
- 适合需要跨环境泛化的离线强化学习场景。
本文研究离线强化学习，旨在训练一个能在动态未知环境间有效泛化的鲁棒策略。提出一种新方法Trajectory Encoding Augmentation（TEA），通过集成来自AutoEncoders等序列编码器的环境动态潜在表示，扩展状态空间。实验表明，结合TEA的策略在面对具有新动态的新环境时，迁移性能优于仅依赖原始状态的方法。结果说明TEA能捕捉关键的环境特异性特征，使强化学习智能体在不同动态条件下实现有效泛化。
原文摘要 · Abstract (English)
In this paper, we investigate offline reinforcement learning (RL) with the goal of training a single robust policy that generalizes effectively across environments with unseen dynamics. We propose a novel approach, Trajectory Encoding Augmentation (TEA), which extends the state space by integrating latent representations of environmental dynamics obtained from sequence encoders, such as AutoEncoders. Our findings show that incorporating these encodings with TEA improves the transferability of a single policy to novel environments with new dynamics, surpassing methods that rely solely on unmodified states. These results indicate that TEA captures critical, environment-specific characteristics, enabling RL agents to generalize effectively across dynamic conditions.
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