Rethinking the Suitability of Reinforcement Learning Algorithms Under Practical Transfer Constraints
Transfer-oriented reinforcement learning requires evaluating algorithms along dimensions that go beyond standard sample efficiency. We focus on two dimensions: practical efficiency, which asks whether conclusions about algorithm suitability change under wall-clock rather than interaction-based budgets, and robustness under dynamics mismatch, which asks how different learning paradigms respond to variability in the training distribution induced by domain randomization. We provide two insights to reinforcement-learning practitioners. First, comparing the sample efficiency of different algorithms is often an insufficient criterion in transfer-oriented settings. The wall-clock time required t...
| Authors | Hany Hamed, Abhishek Naik, Colin Bellinger et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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