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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Authors: Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,537 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,755 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results demonstrate that MERL significantly outperforms state-of-the-art methods, such as MADDPG, on a number of difficult coordination benchmarks. |
| Researcher Affiliation | Collaboration | 1Intel Labs 2University of California, Irvine 3Oregon State University. |
| Pseudocode | Yes | Algorithm 1 provides a detailed pseudo-code of the MERL algorithm. |
| Open Source Code | Yes | Additionally, our source code 1 is available online. Footnote 1 provides the URL: https://tinyurl.com/y6erclts |
| Open Datasets | Yes | We adopt environments from (Lowe et al., 2017) and (Rahmattalabi et al., 2016) to perform our experiments. Each environment consists of multiple agents and landmarks in a two-dimensional world. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. It describes periodic testing on task instances but does not define a separate validation set split from the data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, cloud instance types) used to run the experiments. |
| Software Dependencies | No | The paper mentions various algorithms like DDPG, TD3, and MADDPG, and environments, but it does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | The choice of hyperparameters is explained in the Appendix. |