CDRL-Bench: Benchmarking Cross-Domain Transfer in Reinforcement Learning

Cross-domain reinforcement learning (CDRL) aims to improve sample efficiency by reusing knowledge across tasks, environments, and embodiments. However, existing benchmarks largely cover specific subsets of CDRL, such as dynamics variation, while broader settings involving heterogeneous state and action spaces are evaluated less systematically and lack a unified framework. We introduce CDRL-Bench, a unified benchmark for evaluating transfer between heterogeneous source and target Markov decision processes. CDRL-Bench spans four transfer settings: same-task morphology transfer, cross-task sub-skill transfer, transfer from pretrained generalist models, and real-world transfer on physical robotic hardware. Across locomotion, navigation, and robotic manipulation, CDRL-Bench standardizes source–target pairs, interaction budgets, transfer protocols, and evaluation metrics, enabling fair and reproducible comparison across diverse transfer approaches. By broadening evaluation beyond morphology adaptation, CDRL-Bench provides a unified testbed for studying more general forms of cross-domain knowledge transfer.