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..
Adversarial Diffusion for Robust Reinforcement Learning
Authors: Daniele Foffano, Alessio Russo, Alexandre Proutiere
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results across standard benchmarks show that AD-RRL achieves superior robustness and performance compared to existing robust RL methods. |
| Researcher Affiliation | Academia | Daniele Foffano Division of Decision and Control Systems KTH, Royal Institute of Technology EMAIL Alessio Russo Faculty of Computing and Data Sciences Boston University EMAIL Alexandre Proutiere Division of Decision and Control Systems KTH, Royal Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Adversarial Diffusion for Robust Reinforcement Learning (AD-RRL) ... Algorithm 2 Adversarial Diffusion Trajectory Sampling ... Algorithm 3 Diffusion model training |
| Open Source Code | Yes | The official implementation for AD-RRL can be found on Git Hub: https://github.com/ danielefoffano/AD-RRL |
| Open Datasets | Yes | Our experiments are conducted on several optimal control tasks from the Mu Jo Co suite: Inverted Pendulum, Reacher, Hopper, Half Cheetah, and Walker. |
| Dataset Splits | No | All agents are trained in the default Mu Jo Co/Open AI Gym environment (fixed physics), for 1.5M steps. ... At test time, we alter key physics-related parameters and assess the agent s performance against both robust and non-robust baselines. |
| Hardware Specification | Yes | The training of AD-RRL and Polygrad was performed on three different machines. On a cluster node with one A100 GPU, Icelake CPU and 256 GB of RAM. The remaining model-free baselines were trained on a laptop with an Intel i7-1185G7 CPU, Mesa Intel Xe Graphics GPU and 32 GB of RAM. |
| Software Dependencies | No | The implementation of Poly GRAD was taken from the respective github repository [38]. The same was done for CPPO and M2TD3... For TRPO and PPO we used the implementation from Stable-Baselines3 [36]. |
| Experiment Setup | Yes | Table 2: Hyperparameters for A2C training. ... Table 3: Hyperparameters for adversarial diffusion training. |