We propose a training-free projection-based continual merging method that efficiently combines models incrementally.

To incorporate reward shaping approach into multi-task reinforcement learning, we propose a Centralized Reward Agent based MTRL framework (CenRA) to share and transfer knowledge across multiple tasks.