CFlowNets: Continuous Control with Generative Flow Networks
Authors: Yinchuan Li, Shuang Luo, Haozhi Wang, Jianye HAO
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results on continuous control tasks demonstrate the performance advantages of CFlow Nets compared to many reinforcement learning methods, especially regarding exploration ability. (...) To demonstrate the effectiveness of the proposed CFlow Nets, we conduct experiments on several continuous control tasks with sparse rewards, including Point-Robot-Sparse, Reacher-Goal-Sparse, and Swimmer-Sparse. |
| Researcher Affiliation | Collaboration | Yinchuan Li1, Shuang Luo2 , Haozhi Wang3, Jianye Hao1,3 1Huawei Noah s Ark Lab, Beijing, China 2Zhejiang University, Huangzhou, China 3Tianjin University, Tianjin, China |
| Pseudocode | Yes | The pseudocode is provided in Appendix C. (Algorithm 1 Generative Continuous Flow Networks (CFlow Nets) Algorithm is presented in Appendix C.) |
| Open Source Code | Yes | The codes are available at http://gitee.com/mindspore/models/tree/master/research/gflownets/cflownets |
| Open Datasets | Yes | To demonstrate the effectiveness of the proposed CFlow Nets, we conduct experiments on several continuous control tasks with sparse rewards, including Point-Robot-Sparse, Reacher-Goal-Sparse, and Swimmer-Sparse. (...) Both Reacher-Goal-Sparse and Swimmer-Sparse are adapted from Open AI Gym s Mu Jo Co environment. |
| Dataset Splits | No | The paper does not provide specific percentages or sample counts for training, validation, and test splits. It mentions collecting '10000 trajectories' but not how they are partitioned for different phases. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions software components like 'Adam' optimizer but does not provide specific version numbers for any key software libraries, frameworks, or environments used (e.g., PyTorch version, Gym version). |
| Experiment Setup | Yes | We provide the hyper-parameters of all compared methods under different environments in Table 1, Table 2, Table 3, Table 4, and Table 5. (...) As for Total Timesteps , Start Traning Timestep , Max Episode Length , Actor Network Hidden Layers , Critic Network Hidden Layers , Optimizer , Learning Rate , and Discount Factor , we set them the same for all algorithms for a fair comparison. |