← Back to Benchmarks
simmediumlocomotionmetric · varies
Adversarial Fine-tuning in Offline-to-Online Reinforcement Learning for Robust Robot Control
Description
Offline reinforcement learning enables sample-efficient policy acquisition without risky online interaction, yet policies trained on static datasets remain brittle under action-space perturbations such as actuator faults. This study introduces an offline-to-online framework that trains policies on clean data and then performs adversarial fine-tuning, where perturbations are injected into executed actions to induce compensatory behavior and improve resilience. A performance-aware curriculum furth