Cross-Domain Policy Optimization via Bellman Consistency and Hybrid Critics
Cross-domain reinforcement learning (CDRL) is meant to improve the data efficiency of RL by leveraging the data samples collected from a source domain to facilitate the learning in a similar target domain. Despite its potential, cross-domain transfer in RL is known to have two fundamental and intertwined challenges: (i) The source and target domains can have distinct state space or action space, and this makes direct transfer infeasible and thereby requires more sophisticated inter-domain mappings; (ii) The transferability of a source-domain model in RL is not easily identifiable a priori, and hence CDRL can be prone to negative effect during transfer. In this paper, we propose to jointly...
| Authors | Ming-Hong Chen, Kuan-Chen Pan, You-De Huang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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