Rainbow-DemoRL: Combining Improvements in Demonstration-Augmented Reinforcement Learning
Several approaches have been proposed to improve the sample efficiency of online reinforcement learning (RL) by leveraging demonstrations collected offline. The offline data can be used directly as transitions to optimize RL objectives, or offline policy and value functions can first be learned from the data and then used for online finetuning or to provide reference actions. While each of these strategies has shown compelling results, it is unclear which method has the most impact on sample efficiency, whether these approaches can be combined, and if there are cumulative benefits. We classify existing demonstration-augmented RL approaches into three categories and perform an extensive em...
| Authors | Dwait Bhatt, Shih-Chieh Chou, Nikolay Atanasov |
|---|---|
| Primary category | cs.RO |
| Published | 28 March 2026 |
| Last updated at source | 28 March 2026 |
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