FARL: A Fully Automated Pipeline of RL Interface and Solver Design
Existing reinforcement learning (RL) largely focuses on optimizing learning algorithms within a pre-defined task formulation, while applying RL to new tasks still relies heavily on human expertise to design the learning pipeline: what an agent observes, how it is rewarded, which algorithm it uses, and how training is configured. Automating these choices is challenging because they are tightly coupled. We introduce FARL (Fully Automated Reinforcement Learning), a framework that automates RL design by jointly searching observation and reward programs, learning algorithms, and training settings. FARL uses training statistics and observed behavior to diagnose failures, propose changes, and te...
| Authors | Tianyu Jin, Liuyu Xiang, Chaowen Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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