Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Authors: Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |