Flow Q-Learning
Authors: Seohong Park, Qiyang Li, Sergey Levine
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that FQL leads to strong performance across 73 challenging stateand pixel-based OGBench and D4RL tasks in offline RL and offline-to-online RL. |
| Researcher Affiliation | Academia | 1University of California, Berkeley. Correspondence to: Seohong Park <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Flow Q-Learning (FQL) |
| Open Source Code | Yes | We provide our full implementation and exact commands to reproduce the main results of FQL at https://github.com/seohongpark/fql. |
| Open Datasets | Yes | We empirically show the effectiveness of FQL on 73 diverse stateand pixel-based tasks across the recently proposed OGBench (Park et al., 2025) and standard D4RL (Fu et al., 2020) benchmarks. |
| Dataset Splits | No | The paper describes using offline datasets for training and evaluating the policy's performance in the environment but does not specify explicit training/test/validation splits for the datasets themselves. |
| Hardware Specification | Yes | The run times are measured on the same machine using a single A5000 GPU, and are averaged over 8 seeds. |
| Software Dependencies | No | The paper mentions implementing FQL in JAX and using Adam optimizer and GELU nonlinearity, but does not provide specific version numbers for JAX or other key software libraries. |
| Experiment Setup | Yes | We provide the complete list of hyperparameters in Table 5 and task-specific hyperparameters in Tables 6 and 7. |