ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL
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 ...
| Authors | Yikang Gui, Bikramjit Banerjee, Prashant Doshi |
|---|---|
| Primary category | cs.LG |
| Published | 2 June 2026 |
| Last updated at source | 25 September 2026 |
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