Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Distributional Reinforcement Learning with Regularized Wasserstein Loss
Authors: Ke Sun, Yingnan Zhao, Wulong Liu, Bei Jiang, Linglong Kong
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that Sinkhorn DRL consistently outperforms or matches existing algorithms on the Atari games suite and particularly stands out in the multi-dimensional reward setting. |
| Researcher Affiliation | Collaboration | 1University of Alberta, Edmonton, Canada 2 Harbin Engineering University, China 3Huawei Noah s Ark Lab |
| Pseudocode | Yes | Algorithm 1 Generic Sinkhorn distributional RL Update; Algorithm 2 Sinkhorn Iterations to Approximate Wc,ε; Algorithm 3 Sinkhorn Distributional RL |
| Open Source Code | Yes | Code is available in https://github.com/datake/Sinkhorn Dist RL. |
| Open Datasets | Yes | We substantiate the effectiveness of Sinkhorn DRL as described in Algorithm 1 on the entire 55 Atari 2600 games. |
| Dataset Splits | No | The paper states that algorithms are evaluated over 40M training frames and results are averaged over three seeds, but it does not specify explicit train/validation/test dataset splits. |
| Hardware Specification | Yes | We run our experiments on multiple NVIDIA 3090 Ti GPUs |
| Software Dependencies | No | The paper mentions building algorithms based on a 'well-accepted Py Torch implementation' and re-implementing MMD-DQN based on its 'original Tensor Flow implementation', but it does not specify version numbers for these or other key software dependencies. |
| Experiment Setup | Yes | For a fair comparison with QR-DQN, C51, and MMD-DQN, we use the same hyperparameters: the number of generated samples N = 200, Adam optimizer with lr = 0.00005, ϵAdam = 0.01/32. In Sinkhorn DRL, we choose the number of Sinkhorn iterations L = 10 and smoothing hyperparameter ε = 10.0 in Section 5.1 after conducting sensitivity analysis in Section 5.2. Guided by the contraction guarantee analyzed in Theorem 1, we use the unrectified kernel, specifically setting c = kα and choosing α = 2. We evaluate all algorithms on 55 Atari games, averaging results over three seeds. |