让强化学习奖励在新环境组合中快速迁移，提升样本效率。
ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL
- 将环境动态与任务目标解耦为独立隐变量空间
- 在未见过的环境-目标组合上实现少样本奖励迁移
- 适合需要跨场景泛化的强化学习应用
逆强化学习中的奖励迁移在面对未见的环境动态与任务目标组合时不可靠。本文提出基于因子化对比抽象的可迁移逆强化学习框架（ConTraIRL），通过双编码器结构将观测映射至独立的动态与目标隐空间，并采用双对比目标进行训练。时间对齐机制促使动态编码器学习目标无关结构，而目标编码器则捕捉动态无关特征。该分解支持重组后的动态-目标设置下的奖励推断。在连续控制基准测试中，ConTraIRL 在未见过的动态-目标配对上实现有效少样本迁移，相比基线方法显著提升样本效率与奖励恢复能力。
原文摘要 · Abstract (English)
Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals. We propose Factorized Contrastive Abstractions for Transferable IRL (ConTraIRL), a framework that enables compositional reward transfer by learning decoupled latent representations of these two factors. ConTraIRL uses a dual-encoder architecture that maps observations into separate dynamics and goal latent spaces, trained with a dual contrastive objective. Temporal alignment encourages the dynamics encoder to learn goal-invariant structure, while the goal encoder captures dynamics-invariant features. This factorization supports reward inference under recombined dynamics-goal settings. Experiments on continuous control benchmarks demonstrate effective few-shot transfer to unseen dynamics-goal pairings, improving sample efficiency and reward recovery over transfer IRL baselines.
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