Offline Online Hybrid Reinforcement Learning With Replay Buffer Fusion

via “experience replay buffer with prioritized sampling for off-policy learning”

* 🏆 2015: [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (Faster R-CNN)](https://papers.nips.cc/paper/2015/hash/14bfa6bb14875e45bba028a21ed38046-Abstract.html)

Unique: Introduces experience replay as a core stabilization mechanism for deep Q-learning, enabling off-policy updates from a replay buffer rather than on-policy streaming updates. This architectural choice decouples exploration (data collection) from exploitation (learning), allowing the same transition to be used multiple times with different target networks.